Dynamic controllability management method and system for long-term evolution of large model
By constructing a controllable critical region and introducing a dynamic control mechanism with a controllable calibration head, the problems of solidification and divergence in large model training are solved, realizing the controllable management and continuous evolution of the model, and improving the training signal absorption efficiency and inference ability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-10
AI Technical Summary
Existing large model training methods tend to become rigid after multiple rounds of training, making it difficult to maintain controllability and plasticity. This results in a decline in the ability to absorb training signals and a limitation in the improvement of reasoning ability, making it unable to adapt to new tasks and new business needs.
By constructing a controllable critical region, introducing a controllable calibration head and a dynamic adjustment mechanism, the model status is monitored in real time, deviations are actively corrected, and training strategies are dynamically adjusted to ensure that the model is trained within the controllable critical region.
It achieves proactive controllability management of the model, improves the efficiency of training signal absorption and inference ability, enhances the model's performance evolution ability in multiple rounds of training, and has cross-task adaptability and continuous plasticity.
Smart Images
Figure CN121834512A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of artificial intelligence technology and relates to a management method and system for the long-term evolution of large models, and more particularly to a dynamic controllability management method and system for the long-term evolution of large models. Background Technology
[0002] With the large-scale deployment of Large Model Learning (LLM) and Small Model Learning (SLM) in intelligent question answering, knowledge reasoning, code generation, multi-task execution, and enterprise-level intelligent applications, maintaining long-term controllability and continuous adaptability of models during multiple rounds of training, distillation, and task transfer has become a key issue affecting model performance evolution and engineering feasibility. Current models often tend to become behaviorally rigid due to repeated fine-tuning, distillation, or RLHF processes, leading to decreased training signal absorption capacity, limited inference ability improvement, and difficulty in adapting to new tasks or business requirements. To improve model training efficiency and long-term evolution capabilities, the industry has explored various parameter optimization strategies and distillation techniques; however, as the complexity of model applications increases, these existing methods have gradually revealed significant limitations.
[0003] 1. A traditional model optimization system based on static training and distillation process.
[0004] Current mainstream model training and distillation methods are generally based on fixed process designs, such as pre-setting training data distribution, setting fixed loss function weights, using unified inference steps, and adopting static distillation strategies. Although these methods can improve performance in early models, their inherent defects have become increasingly prominent: (1) Lack of controllability adaptation capability: The sensitivity of the model varies at different training stages, but the static process cannot dynamically adjust the optimization intensity according to the model state. (2) Inability to identify whether the model has entered the solidified or divergent region: The plasticity of the model changes with the training rounds, but traditional methods cannot monitor changes in controllability. (3) Difficulty in supporting continuous evolution scenarios: When a new task arrives, the model often needs to be fine-tuned from scratch, which is costly and prone to catastrophic forgetting. Therefore, the traditional static training process is actually a blind optimization system that cannot dynamically manage the controllability of the model.
[0005] 2. Model tuning strategies based on a single loss or a single objective.
[0006] Some studies attempt to improve the generalization ability of models or make training more stable by designing new loss functions (such as stability loss, distillation loss, regularization terms, etc.). However, these methods usually only optimize one aspect of the model's behavior, such as reducing entropy, enhancing sparsity, or guiding the model to converge in a certain direction. Typical problems include: (1) Fixed optimization direction: A single loss will drive the model to continuously shift towards a certain state, but will not consider the balance of controllability. (2) Lack of state feedback mechanism: Whether the model has entered a fixed region that is difficult to adjust cannot be detected and corrected. (3) Cannot be used for multi-round training and long-term evolution: Continuous fine-tuning will accumulate the shift, and the model will gradually become difficult to retrain. Therefore, this type of method is difficult to maintain the continuous plasticity and performance improvement of the model in multiple scenarios, multiple tasks, and multiple stages.
[0007] 3. Passive adjustment scheme based on model monitoring or output constraints.
[0008] In some systems, signals such as the model's output entropy, gradient norm, and training loss changes are monitored to assist in the stability control of the training process. However, such schemes are essentially passive monitoring and do not provide an active adjustment mechanism. Their main shortcomings include: (1) inability to actively intervene in the model's controllability state: even if the model is detected as solidified, it cannot be pulled back to the plastic zone. (2) lack of structured calibration capabilities: there is a lack of a dedicated module to correct the deviation behavior. (3) lack of control over the inference process: the inference chain capability decreases after the model solidifies, but existing monitoring methods cannot repair it. Therefore, such methods lack the ability of active calibration and dynamic adjustment.
[0009] 4. An attempt at plasticity maintenance based on parameter regularization and continuous learning framework.
[0010] Some studies in the field of continuous learning have proposed using regularization (such as EWC), parameter freezing, and LoRA stacking modules to avoid catastrophic forgetting in order to improve the long-term trainability of models. Formally, these methods focus on maintaining model plasticity, but their core problems are: (1) inability to identify the controllability critical region: the existence of multiple controllability states of the model is not defined, and the optimal plasticity interval cannot be determined. (2) lack of independent controllability calibration structure: it is impossible to apply calibrating loss to the model behavior when it deviates from the critical region. (3) lack of dynamic adjustment mechanism: it is impossible to adjust controllability in real time according to task type, training stage, and model state. (4) only focus on forgetting problems rather than controllability enhancement: the effect on performance improvement, inference depth, and task transferability is limited. (5) lack of active optimization mechanism driven by runtime feedback: it is impossible to allow the model to gradually find the optimal controllability point during training. Therefore, although the continuous learning framework is the closest existing technical route to this invention, its essence is still based on a passive strategy to avoid forgetting.
[0011] In summary, existing model optimization systems generally suffer from the following three fundamental shortcomings: (1) Fixed training behavior and lack of proactive and controllable adjustment capabilities: Once a model enters a fixed state, traditional optimization methods cannot pull it back to a highly malleable region. (2) Inability to perform multi-objective collaborative optimization: It is difficult to simultaneously consider multiple objectives such as inference ability, training efficiency, generalization ability, and parameter responsiveness. (3) Lack of sustainable evolution mechanism: Existing solutions essentially operate within a static training framework and cannot achieve continuous evolution based on state feedback.
[0012] Given the aforementioned shortcomings of existing technologies, there is an urgent need to research a dynamic controllability management method and system for the long-term evolution of large models. Summary of the Invention
[0013] To overcome the shortcomings of existing technologies, this invention proposes a dynamic controllability management method and system for the long-term evolution of large models. By constructing the controllability critical region of the model and quantifying the controllable state of the model, introducing a controllability calibration head to actively correct deviation behavior, and designing a dynamic adjustment mechanism to adjust the controllability level of the model in real time, it realizes a paradigm leap from uncontrollable, difficult to adjust, and easily solidified models to models with the potential for sustainable evolution, high plasticity, and high performance enhancement. This provides a brand-new basic technical framework for the core capabilities of large models, such as inference enhancement, task transfer, distillation efficiency, and continuous learning.
[0014] To achieve the above objectives, the present invention provides the following technical solution: A method for managing the dynamic controllability of large-scale models over long periods of time, characterized by the following steps: S1: Obtain the current state information of the large model, including model parameters, task category, and training stage; S2: Calculate the controllability score of the large model based on the output distribution entropy, distribution change, and logits drift of the large model, and determine whether the current state of the large model deviates from the controllability critical region based on the controllability score. If it deviates from the controllability critical region, proceed to S3; otherwise, proceed directly to S4. S3: Actively corrects the deviation behavior of large models during training through the controllability calibration head, so that its current state does not deviate from the controllability critical region; S4: Dynamically adjust the training strategy of the large model based on the controllability score and task category.
[0015] Preferably, the calculation of the controllability score of the large model based on the output distribution entropy, distribution change, and logits drift in step S2 is specifically as follows: S21: Calculate the output distribution entropy of the large model : , In the formula, This represents the probability of the i-th class in the output of the large model. This represents the logarithm of the probability for the i-th category; S22: Calculate the change in distribution : , In the formula, Indicates the large model in the first... Output probability distribution during training steps Indicates the large model in the first... Output probability distribution during training steps express and KL divergence; S23: Calculate logits drift : , In the formula, Indicates the large model in the first... The logits output during training. Indicates the large model in the first... The logits output during training. express and The Euclidean distance; S24: Calculate the controllability score : , In the formula, , and It is the weighting coefficient.
[0016] Preferably, in step S2, determining whether the current state of the large model deviates from the controllability critical region based on the controllability score specifically involves: if If the current state of the large model does not deviate from the controllability critical region, then it does not; otherwise, it deviates from the controllability critical region. and These are the preset minimum and maximum controllability scores, respectively.
[0017] Preferably, step S3 specifically includes: S31: Determine calibration loss : , In the formula, This represents the optimal controllable target value for the controllability critical region. The intensity coefficient for calibrating the loss; S32: Backpropagate the calibration loss and the original training loss together to update the model parameters: , In the formula, Indicates the first Step model parameters, Indicates the first Step model parameters, For learning rate, This represents the total loss function, which is the original training loss plus the calibration loss. , Represents the total loss function For model parameters The gradient.
[0018] Preferably, in step S31, The task category is dynamically determined.
[0019] Preferably, the training strategy of dynamically adjusting the large model based on the controllability score and task category in step S4 specifically involves: dynamically adjusting the learning rate and loss weights during large model training based on the controllability score and task category.
[0020] Preferably, the dynamic adjustment of the learning rate and loss weights during large model training based on the controllability score and task category is specifically as follows: In contrast, if the controllability score is low, the learning rate is reduced and the stability loss weight is increased; if the controllability score is high, the learning rate is increased and the plasticity loss weight is enhanced.
[0021] Furthermore, the present invention also provides a dynamic controllability management system for the long-term evolution of large models, characterized in that it includes: The state information acquisition module is used to acquire the current state information of the large model, including model parameters, task category, and training stage. The controllable state analysis module is used to calculate the controllability score of the large model based on the output distribution entropy, distribution change and logits drift of the large model, and to determine whether the current state of the large model deviates from the controllability critical region based on the controllability score; The controllability calibration head correction module is used to actively correct the deviation behavior of large models during training through the controllability calibration head, so that its current state does not deviate from the controllability critical region. The dynamic adjustment module is used to dynamically adjust the training strategy of the large model based on the controllability score and task category.
[0022] Furthermore, the present invention also provides a dynamic controllability management device for the long-term evolution of large models, characterized in that it comprises: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic controllability management method for the long-term evolution of large models as described above. Finally, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the steps of the dynamic controllability management method for the long-term evolution of large models as described above.
[0023] Compared with the existing large-scale model training system of "static training process + single loss optimization + fixed fine-tuning strategy", the dynamic controllability management method and system for long-term evolution of large models proposed in this invention has the following significant advantages in terms of maintaining model plasticity, improving inference ability, optimizing distillation efficiency and long-term evolution capability, based on the construction of controllability critical region, controllable calibration head and dynamic adjustment mechanism: 1. Upgrade from static training to controllable perceptual training to achieve proactive adjustment of model behavior and deep performance improvement.
[0024] Traditional model training systems, based on fixed loss and fixed parameter updates, cannot identify whether the model enters a fixed or divergent region during training, thus lacking real-time management of model plasticity. This invention constructs a unified controllability scoring system to explicitly model the model's output distribution, entropy characteristics, and behavioral biases, and further defines a controllability critical region as the state space for maintaining optimal model plasticity. Based on this, a controllability calibration head performs real-time calibration of model deviations, transforming the training process from passive convergence to active plasticity maintenance.
[0025] This mechanism continuously monitors the model's state across multiple stages, including training, distillation, and inference. It dynamically applies calibration loss based on whether the model deviates from the controllability critical region, guiding the model to maintain the structural state best suited for performance improvement. This process not only improves the efficiency of training signal absorption but also significantly enhances the model's inference extensibility in complex tasks, upgrading the model from passive convergence to controlled learning and fundamentally strengthening its performance evolution capability across multiple training rounds.
[0026] 2. The optimization objective has been upgraded from a single objective to a synergistic optimization of plasticity, stability, and performance, enabling more refined control over model capabilities.
[0027] Existing optimization strategies typically focus on a single direction, such as reducing loss, minimizing forgetting, or improving stability, but they cannot simultaneously address both plasticity and performance improvement goals. This can easily lead to rigid model output, insufficient inference depth, or decreased multi-task adaptability. The controllability critical region mechanism established in this invention treats the plasticity state itself as one of the explicit optimization objectives, making the model's trainability a key, monitorable, and adjustable indicator.
[0028] During training, the dynamic control mechanism of this invention can automatically adjust the calibration intensity according to the task type and the model's own controllability score. In inference tasks, it enhances controllability to improve inference chain capability; in safe scenarios, it strengthens stability to prevent output drift; and in multi-task environments, it dynamically balances performance and stability, thereby achieving a synergistic optimality among plasticity, stability, and task performance.
[0029] With the help of the multi-factor joint regulation structure proposed in this invention, the large model has significantly enhanced cross-task adaptability and can automatically select the most suitable controllability level in different business scenarios, breaking through the performance bottleneck of traditional single-objective training methods.
[0030] 3. Upgrade from static parameter training to a feedback-driven sustainable evolution mechanism to build a highly plastic model system that can be optimized over the long term.
[0031] Existing large-scale model training methods typically lack the ability to continuously optimize based on runtime feedback after deployment. They cannot dynamically adjust the model state according to new tasks, changes in scenarios, or user needs, resulting in a static model lifecycle where the model is trained once and then discarded. This invention introduces a controllable calibration head and a dynamic adjustment mechanism, enabling the model to receive feedback signals in real time and automatically adjust its controllability state during each round of training, distillation, and even inference.
[0032] When the model experiences insufficient absorption of training signals, stagnant performance gains, or a shortened inference chain, a calibration mechanism can be triggered by the controllable critical region offset detection to bring the model back to the optimal plasticity region, thereby maintaining continuous trainability. Simultaneously, in multi-round task adaptation scenarios, the dynamic adjustment mechanism of this invention can continuously strengthen effective plasticity states and suppress unfavorable offsets, enabling the model to form a closed-loop evolution path of training-feedback-calibration-re-optimization.
[0033] By leveraging this feedback-driven evolutionary mechanism, this invention breaks through the static limitations of traditional model training, enabling large models to maintain long-term plasticity, continuously improve performance, and quickly adapt to new tasks, thus achieving a fundamental upgrade from statically optimized models to a self-evolving model system.
[0034] In summary, this invention achieves a holistic technological upgrade across three levels: model structure, training strategy, and long-term evolution capability, by constructing a controllable critical region, introducing a controllable calibration head, and designing a dynamic adjustment mechanism. This enables large models to maintain high plasticity, high stability, and continuous performance improvement capabilities throughout multiple training rounds and multi-task applications. This invention not only transforms model training from the traditional static convergence paradigm to an active adjustment mode based on controllable states, but also ensures that the model avoids entering the solidified or divergent regions through real-time calibration and feedback-driven mechanisms, thereby significantly improving responsiveness to training signals and inference capabilities. Simultaneously, the dynamic adjustment mechanism allows the model to adaptively adjust its controllability level according to task type and stage requirements, effectively enhancing cross-task transferability and generalization performance. Without altering the main model structure, this invention achieves long-term trainability, low-cost fine-tuning capabilities, and multi-scenario adaptability, providing a novel technical path for building a new generation of intelligent model systems with continuous evolution capabilities, and possessing significant engineering value and promising industrial application prospects. Attached Figure Description
[0035] Figure 1 This is a flowchart of the dynamic controllability management method for long-term evolution of large models according to the present invention.
[0036] Figure 2 This is a schematic diagram of the dynamic controllability management system for long-term evolution of large models according to the present invention.
[0037] Figure 3 This is a structural block diagram of the dynamic controllability management device for long-term evolution of large models according to the present invention. Detailed Implementation
[0038] Before detailing any embodiment of the invention, it should be understood that the invention, in its application, is not limited to the details of the construction and arrangement of the components set forth in the following description or illustrated in the following figures. The invention can have other embodiments and can be practiced or carried out in various ways. Furthermore, it should be understood that the wording and terminology used herein are for descriptive purposes and should not be considered limiting. The use of “comprising” or “having” and variations thereof in this invention is intended to cover the items set forth below and their equivalents, as well as any additional items. Unless otherwise specified or limited, the terms “installation,” “connection,” “support,” and “linkage,” and variations thereof are used broadly and cover both direct and indirect installation, connection, support, and linking. Moreover, “connection” and “linkage” are not limited to physical or mechanical connections or links. Furthermore, firstly, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention. Secondly, the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple. The term "a" should not be construed as a limitation on the quantity.
[0039] With the widespread deployment of large model learning (LLM) and small model learning (SLM) in question answering systems, code generation, knowledge reasoning, and multi-task decision-making, the continuous controllability, task adaptability, and low-cost, rapid alignment of models have become critical technical issues that urgently need to be addressed. Existing model training, distillation, and alignment techniques are mostly based on static optimization methods, which cannot maintain the high plasticity of models in dynamic environments and multi-round tasks, exhibiting the following technical pain points: 1. The model tends to become rigid after multiple rounds of training, making it difficult to quickly align again.
[0040] After undergoing distillation, fine-tuning, or RLHF training, the output behavior of existing large language models tends to stabilize and solidify, and the model's parameter space enters a region that is difficult to readjust again, resulting in a significant decrease in adaptability to new tasks or new security strategies. To re-adapt the model to new task requirements, large-scale training resources are typically required again, which not only increases time and computational costs but also makes it difficult for the model to achieve continuous iteration and flexible deployment in real-world complex scenarios. With each training epoch, the model's plasticity and adjustability decrease, making it difficult to meet the urgent needs of dynamic task environments for rapid model updates and lightweight alignment.
[0041] 2. The small model lacks secondary controllability after distillation.
[0042] Small models are typically trained to perform optimally for specific tasks during the distillation and compression process. However, this process often leads to excessive contraction of their output behavior patterns, resulting in severe pattern rigidity. The distilled model exhibits extremely low plasticity when adding new tasks, switching tasks, or adjusting inference methods, making secondary optimization difficult and hindering its flexible reuse across multiple users and scenarios. Due to this lack of flexibility, small models often require retraining when facing new domains, complex tasks, or constantly changing business scenarios, severely limiting their long-term deployment value under resource-constrained conditions and failing to meet the requirements of enterprise-level scenarios for low cost, multi-tasking, and dynamic adaptability.
[0043] 3. Lack of dynamic control mechanisms for controllable states.
[0044] Existing large-scale model training and alignment methods do not consider the fact that models may enter controllable or uncontrollable regions during training, nor do they have a mechanism to identify the current controllability state of the model. During continuous training or distillation, the model may gradually evolve into a stable region that is difficult to modify, significantly reducing the sensitivity of parameters to fine-tuning signals and resulting in a significant weakening of the model's adjustability in subsequent tasks. Due to the lack of dynamic adjustment mechanisms, the model cannot be actively maintained in the controllable region most suitable for rapid fine-tuning in future tasks, thus allowing the accumulated bias from multiple training rounds to continuously increase, ultimately causing the model to become rigid, difficult to update, and unable to meet the requirements of flexibility and adaptability in multi-task environments.
[0045] To address these issues, this invention proposes a dynamic controllability management method for the long-term evolution of large models. This method is based on the controllability critical zone, and for the first time introduces the controllability critical zone into the training and inference system of large models. By constructing model controllability indices, a critical zone calibration mechanism, and dynamic adjustment strategies, the model is kept in a state where it can quickly and cost-effectively adapt to new tasks, ensuring the model possesses continuous controllability and long-term evolutionary capabilities. Its core technical solution is as follows: 1. Construction of the controllability critical region.
[0046] This invention first designs a controllability scoring mechanism to continuously evaluate the controllability state of a model during training and inference. This mechanism comprehensively analyzes multi-dimensional information such as the model's output distribution, logits entropy, and task offset to determine whether the model's current state is in an over-fixed, over-divergent, or optimally controllable region (i.e., a controllability critical region). An over-fixed region indicates that the model's behavior is difficult to adjust further, while an over-divergent region signifies unstable output and insufficient sensitivity to task constraints. Through real-time calculation of the controllability score, this invention can accurately identify whether the model is in the critical region most suitable for future fine-tuning, distillation, or task migration, providing a basis for subsequent dynamic control and calibration.
[0047] 2. Introduction of controllable calibration head.
[0048] Building upon controllability scoring, this invention further proposes a controllability calibration head to detect whether the model deviates from the controllability critical region and perform real-time calibration. The controllable calibration head is an auxiliary structure used to correct the state of a large model. When the model deviates from the controllability critical region, the controllable calibration head applies a calibration signal to bring the model back to the state most suitable for training and inference. It can be embedded into any training framework such as fine-tuning, distillation, and RLHF without altering the main structure. When the model's current state tends to gradually shift towards a fixed or divergent region, the controllable calibration head introduces a corresponding calibration loss to guide the model back to the highly malleable controllable critical region, ensuring its easily adjustable characteristics throughout the training and inference process. This calibration structure can be seamlessly integrated into alignment processes such as distillation, fine-tuning, layer-by-layer inference chain alignment, and RLHF / DPO without any modification to the original model architecture, thus possessing high versatility and deployment flexibility. This invention utilizes this calibration head to achieve continuous maintenance of model controllability, enabling the model to maintain stable yet malleable performance during multi-stage training.
[0049] 3. Dynamic regulation mechanism.
[0050] During model training and distillation, this invention employs a dynamic adjustment mechanism to regulate the model's controllability in real time. This mechanism dynamically changes the calibration intensity based on the current task type and controllability score, ensuring the model remains within the most manageable controllability threshold. For example, in reasoning tasks, the model is guided to maintain high controllability, making it easier to form scalable inference chains. In security-sensitive tasks, the dynamic adjustment mechanism enhances the overall stability of the model's behavior to reduce potential drift. In multi-task scenarios, this mechanism adaptively balances controllability and stability, ensuring the model can quickly switch between different tasks without conflict. Through this continuous dynamic adjustment, the model can smoothly transition between different training objectives, significantly improving task adaptability.
[0051] Based on the three core technologies mentioned above, this invention enables large models to maintain high plasticity after multiple rounds of training, avoiding performance solidification; it improves the model's sensitivity to training signals, thereby enhancing inference capabilities; it allows for dynamic adjustment of performance in multiple tasks and scenarios, significantly enhancing generalization ability; and it greatly reduces the cost of readjustment, enabling long-term model evolution and continuous optimization. This invention can significantly improve model performance without changing the main structure of the model, possessing strong application value and engineering significance.
[0052] Figure 1 A flowchart of the dynamic controllability management method for long-term evolution of large models according to the present invention is shown. Figure 1 As shown, the dynamic controllability management method for long-term evolution of large models according to the present invention includes the following steps: S1: Obtain status information.
[0053] Obtain the current state information of the large model, including model parameters, task category, and training stage.
[0054] In this invention, the first step is to obtain the current state information of the large model, including model parameters, task category, and current training stage. Let the initial model parameters be... The task type is The training phase is The current state information of the large model can then be represented as: .
[0055] In this stage, no training is performed on the large model; instead, it provides the necessary initial conditions for subsequent controllability analysis and regulation. This step is similar to the system preparation phase, ensuring that the large model has complete and monitorable state information before entering the controllability analysis stage. Depending on the task type, the large model may face different inference requirements, security constraints, or generation goals; therefore, the task type also determines the reference strategy for subsequent regulation.
[0056] S2: Controlled state analysis.
[0057] In this step, the large model is assessed to determine whether it is currently in a stagnant, divergent, or controllable critical region. This judgment is based on a quantitative analysis of the large model's output distribution, behavioral changes, and training responsiveness.
[0058] 1. Calculate the output distribution entropy of the large model. , to measure the uncertainty of large models.
[0059] Output distribution entropy of large model It is an important indicator for judging whether a large model has become rigid or divergent: , In the formula, This represents the probability of the i-th class in the output of the large model. This represents the logarithm of the probability for the i-th category. Output the distribution entropy. A low value indicates that the large model is overconfident (rigid), and the output distribution entropy is low. A high value indicates that the model is unstable (divergent).
[0060] 2. Calculate the change in distribution To determine whether training has stagnated or diverged.
[0061] In this invention, the KL divergence of the output probability distribution of a large model over two consecutive steps is defined as the distribution change. : , In the formula, Indicates the large model in the first... Output probability distribution during training steps Indicates the large model in the first... Output probability distribution during training steps express and The KL divergence. If This indicates that training has stalled; if If the size is too large, the large model will begin to exhibit unstable divergent behavior.
[0062] 3. Calculate logits drift Assess behavioral deviation.
[0063] , In the formula, Indicates the large model in the first... The logits output during training. Indicates the large model in the first... The logits output during training. express and The Euclidean distance. This is used to measure the difference between the logits vectors of the large model's outputs in two separate instances. A large difference indicates a significant behavioral shift in the large model from the previous time step to the current time step; a small difference indicates that the large model's predictions are relatively stable.
[0064] 4. Calculate the controllability score .
[0065] The controllability score of the model is obtained by combining the above three indicators according to their weights. : .
[0066] In the formula, , and These are weighting coefficients, each corresponding to the output distribution entropy. Distribution variation and logits drift The relative importance of the three indicators. Adjustment... , and The contribution of the three metrics to the final controllability score can be flexibly adjusted according to different task types. Specifically, when more attention needs to be paid to the uncertainty of the large model output, the contribution can be increased. When greater emphasis is placed on the stability and efficient updating of large model training processes, the size can be increased. When it is necessary to place greater emphasis on the degree of shift in the internal representation of a large model, the size can be increased. The weighted structure composed of three coefficients enables this invention to dynamically adapt to different controllability requirements in multiple tasks and scenarios, ensuring that the large model can be accurately distinguished between the solidified, divergent, and controllability critical regions.
[0067] In this invention, the controllability score is calculated. Then, a controllability score can be calculated. Determine the current state of the large model: : Cured area (difficult to adjust); : Divergence region (unstable); : Controllability critical zone (optimal training area).
[0068] in, and These are the preset minimum and maximum controllability scores, which can be preset as needed.
[0069] Therefore, based on the controllability score, it can be determined whether the current state of the large model deviates from the controllability critical region. Specifically, if... If the current state of the large model does not deviate from the controllability critical region, then it does not deviate from the controllability critical region.
[0070] In this invention, when the current state of the large model does not deviate from the controllability critical region, dynamic adjustment can be directly performed. However, when the current state of the large model deviates from the controllability critical region, it is necessary to actively correct the deviation behavior of the large model during training through the controllability calibration head so that its current state does not deviate from the controllability critical region.
[0071] S3: Controllable calibration head correction.
[0072] When the current state of a large model deviates from the controllability critical region, the controllability calibration head actively corrects the deviation behavior that occurred during the training process, ensuring that its current state does not deviate from the controllability critical region. Specifically, this includes: 1. Determine calibration loss : , In the formula, This represents the optimal controllable target value for the controllability critical region. The intensity coefficient is used to calibrate the loss and can be set as needed.
[0073] The calibration loss Used to force the controllability of a large model back to the center of the controllability critical region.
[0074] 2. Backpropagate the calibration loss and the original training loss together to update the model parameters: , In the formula, Indicates the first Step model parameters, Indicates the first Step model parameters, For learning rate, This represents the total loss function, which is the original training loss plus the calibration loss. , Represents the total loss function For model parameters The gradient.
[0075] In this invention, = Original training loss + calibration loss In other words, the calibration loss increases the original loss function of the model, making it a common training objective. The larger model, through backpropagation, directs the parameters towards... The directional adjustment involves allowing model parameters to change under the influence of calibration during training. In this stage, the large model is essentially pulled back onto the correct track. By automatically determining whether the large model is too "rigid" (fixed) or too "chaotic" (divergent), appropriate correction signals are applied to bring it back to the optimal optimization position (the controllability critical region). This makes large model training more robust, preventing it from losing trainability due to excessive training time or changes in task distribution.
[0076] S4: Dynamic control.
[0077] The training strategy of the large model is dynamically adjusted based on the controllability score and task category, and the large model is trained based on the training strategy.
[0078] The purpose of this stage is to allow the large model to automatically adjust its controllability based on the task category and its own state, thereby achieving the best training results or inference performance.
[0079] 1. Task-driven selection of controllable objectives.
[0080] Dynamically determined based on the task category In reasoning tasks, the large model is guided to maintain high controllability, making it easier to form scalable inference chains. In security tasks, the dynamic adjustment mechanism improves the stability of the overall model behavior to reduce potential drift. In multi-task scenarios, this mechanism adaptively balances controllability and stability, ensuring that the large model can quickly switch between different tasks without conflict. Through this continuous dynamic adjustment, the large model can smoothly transition between different training objectives, significantly improving task adaptability.
[0081] 2. Dynamic optimization.
[0082] Based on the current controllability score Adaptive adjustment of training configuration based on task type: ; .
[0083] in: This is the learning rate adjustment function; This is the loss weight adjustment function; Task type; The learning rate; This is the loss weight.
[0084] Specifically, for more complex task types, there is a greater tendency to improve the controllability of large models; for more security-sensitive task types, there is a greater tendency to improve the stability of large models. In other words, the controllability score of the large model is considered. and task type To automatically adjust the training strategy (learning rate and loss weights). If the large model is currently too scattered ( If the learning rate is too low, the model will automatically reduce the learning rate and increase the stability loss weights; if the large model is too rigid ( (If the value is too high), it will automatically increase the learning rate and enhance the plasticity loss weight.
[0085] This is an adaptive training adjustment mechanism. In simpler terms, this mechanism can be understood as follows: the controllability of the large model is to enhance its ability to handle complex scenarios across different task types, where most aspects can be fine-tuned, making the large model highly adaptable. Then, in scenarios involving greater safety and sensitivity, where it's undesirable to arbitrarily change the large model's parameters, the stability of the large model needs to be enhanced.
[0086] S5: Outputs large models with good controllability.
[0087] Through dynamic adjustments, the large model will eventually be adjusted to the optimal controllability range: .
[0088] Therefore, large models with good output controllability have the following advantages: stronger reasoning ability, faster ability to adapt to new tasks, lower training cost, and long-term plasticity retention.
[0089] This invention forms a closed loop through state recognition, calibration and correction, and dynamic regulation, enabling large models to maintain optimal controllability during training, distillation, and inference, thereby achieving long-term sustainable evolution of large models.
[0090] Figure 2 A schematic diagram of the dynamic controllability management system for long-term evolution of large models according to the present invention is shown. Figure 2 As shown, the dynamic controllability management system for long-term evolution of large models of the present invention includes: 1. Status information acquisition module.
[0091] The state information acquisition module is used to acquire the current state information of the large model, including model parameters, task category, and training stage.
[0092] 2. Control state analysis module.
[0093] The controllable state analysis module is used to calculate the controllability score of the large model based on the output distribution entropy, distribution change, and logits drift of the large model, and to determine whether the current state of the large model deviates from the controllability critical region based on the controllability score.
[0094] 3. Controllable calibration head correction module.
[0095] The controllability calibration head correction module is used to actively correct the deviation behavior of the large model during training, so that its current state does not deviate from the controllability critical region.
[0096] 4. Dynamic control module.
[0097] The dynamic adjustment module is used to dynamically adjust the training strategy of the large model based on the controllability score and task category, and to train the large model based on the training strategy.
[0098] Furthermore, this invention also provides a device for managing the dynamic controllability of long-term evolution of large models. For example... Figure 3As shown, the dynamic controllability management device for long-term evolution of large models according to the present invention includes: a memory 11 for storing one or more programs; one or more processors 12; when the one or more programs are executed by the one or more processors 12, the one or more processors 12 implement the dynamic controllability management method for long-term evolution of large models according to the present invention. Finally, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the dynamic controllability management method for long-term evolution of large models in the present invention.
[0099] The computer-readable storage medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined in this invention, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0100] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0101] The steps of the methods or algorithms described in conjunction with the embodiments disclosed in this invention can be implemented in hardware, software modules executed by a processor, or a combination of both. The software modules can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.
[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Those skilled in the art can modify or make equivalent substitutions to the technical solutions of the present invention based on the concept of the present invention, without departing from the essence and scope of the technical solutions of the present invention.
Claims
1. A method for managing the dynamic controllability of long-term evolution of large models, characterized in that, Includes the following steps: S1: Obtain the current state information of the large model, including model parameters, task category, and training stage; S2: Calculate the controllability score of the large model based on the output distribution entropy, distribution change, and logits drift of the large model, and determine whether the current state of the large model deviates from the controllability critical region based on the controllability score. If it deviates from the controllability critical region, proceed to S3; otherwise, proceed directly to S4. S3: Actively corrects the deviation behavior of large models during training through the controllability calibration head, so that its current state does not deviate from the controllability critical region; S4: Dynamically adjust the training strategy of the large model based on the controllability score and task category.
2. The dynamic controllability management method for long-term evolution of large models according to claim 1, characterized in that, The controllability score of the large model calculated in step S2 based on the output distribution entropy, distribution change, and logits drift of the large model is as follows: S21: Calculate the output distribution entropy of the large model : ， In the formula, This represents the probability of the i-th class in the output of the large model. This represents the logarithm of the probability for the i-th category; S22: Calculate the change in distribution : ， In the formula, Indicates the large model in the first... Output probability distribution during training steps Indicates the large model in the first... Output probability distribution during training steps express and KL divergence; S23: Calculate logits drift : ， In the formula, Indicates the large model in the first... The logits output during training. Indicates the large model in the first... The logits output during training. express and The Euclidean distance; S24: Calculate the controllability score : ， In the formula, , and It is the weighting coefficient.
3. The dynamic controllability management method for long-term evolution of large models according to claim 2, characterized in that, In step S2, determining whether the current state of the large model deviates from the controllability critical region based on the controllability score specifically involves: if If the current state of the large model does not deviate from the controllability critical region, then it does not; otherwise, it deviates from the controllability critical region. and These are the preset minimum and maximum controllability scores, respectively.
4. The dynamic controllability management method for long-term evolution of large models according to claim 3, characterized in that, Step S3 specifically includes: S31: Determine calibration loss : ， In the formula, This represents the optimal controllable target value for the controllability critical region. The intensity coefficient for calibrating the loss; S32: Backpropagate the calibration loss and the original training loss together to update the model parameters: ， In the formula, Indicates the first Step model parameters, Indicates the first Step model parameters, For learning rate, This represents the total loss function, which is the original training loss plus the calibration loss. , Represents the total loss function For model parameters The gradient.
5. The dynamic controllability management method for long-term evolution of large models according to claim 4, characterized in that, In step S31 The task category is dynamically determined.
6. The method for dynamic controllability management of long-term evolution of large models according to claim 5, characterized in that, The specific steps of step S4, which involve dynamically adjusting the training strategy of the large model based on the controllability score and task category, are as follows: dynamically adjusting the learning rate and loss weights during the training of the large model based on the controllability score and task category.
7. The dynamic controllability management method for long-term evolution of large models according to claim 6, characterized in that, The learning rate and loss weights during large model training are dynamically adjusted based on the controllability score and task category, specifically as follows: In contrast, if the controllability score is low, the learning rate is reduced and the stability loss weight is increased. If the controllability score is too high, increase the learning rate and strengthen the plasticity loss weights.
8. A dynamic controllability management system for the long-term evolution of large models, characterized in that, include: The state information acquisition module is used to acquire the current state information of the large model, including model parameters, task category, and training stage. The controllable state analysis module is used to calculate the controllability score of the large model based on the output distribution entropy, distribution change and logits drift of the large model, and to determine whether the current state of the large model deviates from the controllability critical region based on the controllability score. The controllability calibration head correction module is used to actively correct the deviation behavior of large models during training through the controllability calibration head, so that its current state does not deviate from the controllability critical region. The dynamic adjustment module is used to dynamically adjust the training strategy of the large model based on the controllability score and task category.
9. A dynamic controllability management device for the long-term evolution of large models, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic controllability management method for long-term evolution of large models as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the dynamic controllability management method for the long-term evolution of large models as described in any one of claims 1-7.