A reinforcement learning training parameter automatic tuning system and method based on TensorBoard log driving and large model service
By constructing a closed-loop optimization system based on TensorBoard log-driven data acquisition and reinforcement learning hyperparameter tuning, the system solves the problems of low efficiency and high cost in setting hyperparameters in large models and reinforcement learning training, achieving efficient hyperparameter tuning and model performance improvement, and lowering the technical threshold.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- QINGDAO INSPUR HAIRUO ARTIFICIAL INTELLIGENCE CO LTD
- Filing Date
- 2025-10-23
- Publication Date
- 2026-07-10
AI Technical Summary
In the training process of large models and reinforcement learning, hyperparameter setting relies on human experience, which is inefficient, costly, and difficult to cover the global optimal solution. Moreover, existing TensorBoard tools lack automated hyperparameter tuning capabilities, and the hyperparameter tuning strategy lags behind the model training status.
By using TensorBoard log-driven data collection, structured parsing, semantic encoding, and reinforcement learning-based parameter tuning, a closed-loop optimization system is built to generate parameter tuning strategies in real time and dynamically update model parameters. Combined with large model services, end-to-end optimization is achieved.
It improves parameter tuning efficiency, shortens parameter tuning time, enhances model performance, reduces R&D costs, and lowers the technical threshold, enabling SMEs to apply advanced AI technologies.
Smart Images
Figure CN121303240B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of artificial intelligence and reinforcement learning technology, specifically to an automated tuning system and method for reinforcement learning training parameters based on TensorBoard log-driven and large model services. Background Technology
[0002] In the current booming development of artificial intelligence, large models and reinforcement learning have become core forces driving technological breakthroughs and industrial transformation. Large models, with their massive number of parameters and powerful generalization capabilities, have demonstrated outstanding performance in multiple fields such as natural language processing, computer vision, and speech recognition. For example, the GPT series models have achieved unprecedented success in text generation and understanding tasks, and the CLIP model has achieved accurate cross-modal alignment of images and text. Reinforcement learning, through the interactive learning between agents and their environment, has achieved significant breakthroughs in intelligent decision-making and control. AlphaGo's victory over top human players in Go demonstrated the powerful potential of reinforcement learning in complex decision-making scenarios.
[0003] However, combining large models with reinforcement learning faces numerous challenges. Training large models requires massive computing resources and complex distributed architectures; for example, GPT-3 training required tens of thousands of GPUs for parallel computation over several months. During reinforcement learning training, the setting of hyperparameters (such as learning rate, exploration rate, and discount factor) directly affects the model's convergence speed and final performance. However, traditional hyperparameter tuning methods rely on human experience, resulting in low efficiency, high cost, and difficulty in covering the global optimum. For instance, in autonomous driving reinforcement learning training, hyperparameters need to be dynamically adjusted to adapt to complex environments under different road conditions and traffic rules; manual tuning is insufficient to meet real-time requirements.
[0004] In existing technologies, TensorBoard, as a visualization tool for TensorFlow, can provide visualizations of key information such as model structure, activation values, and loss functions, helping developers understand the model training process. However, its functionality is limited to data display and lacks automated hyperparameter tuning capabilities. Some studies have attempted to introduce algorithms such as Bayesian optimization into reinforcement learning hyperparameter tuning, but they have not fully utilized the real-time feedback information in the training logs, resulting in hyperparameter tuning strategies lagging behind the model training status. Furthermore, under the trend of large-scale model service, how to deeply integrate the capabilities of large models with reinforcement learning hyperparameter tuning systems to achieve end-to-end optimization remains an unsolved technical challenge. Summary of the Invention
[0005] The purpose of this invention is to provide an automated system and method for tuning reinforcement learning training parameters based on TensorBoard log-driven and large model services. By parsing training logs in real time and dynamically generating tuning strategies, it achieves closed-loop optimization of reinforcement learning training, thereby improving training efficiency and model performance, and solving the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: an automated tuning system for reinforcement learning training parameters based on TensorBoard log-driven and large model services, the system comprising:
[0007] The data acquisition module is used to collect TensorBoard logs, environmental interaction data, and model parameter snapshots in real time during the reinforcement learning training process.
[0008] The log parsing module performs structured parsing on the collected logs, extracting scalar metrics, histogram statistics, model structure information, and event timestamps to construct multidimensional feature vectors.
[0009] The large model service module performs semantic encoding and context modeling on feature vectors based on a pre-trained language model, and generates parameter adjustment suggestions.
[0010] The reinforcement learning parameter tuning module translates parameter adjustment suggestions into specific parameter tuning actions and optimizes the parameter tuning strategy through reinforcement learning algorithms.
[0011] The feedback optimization module backpropagates errors based on model performance evaluation results and dynamically updates the parameters of the large model and the parameter tuning proxy strategy.
[0012] Preferably, the log parsing module includes:
[0013] The scalar metric extraction submodule reads scalar data such as loss function value, accuracy, and learning rate from the logs and stores them as a multidimensional array in time series order.
[0014] The histogram analysis submodule parses histogram data of weights, gradients, and activation values, and calculates the mean, variance, and skewness statistics.
[0015] The model structure analysis submodule extracts features such as the number of model layers, the number of parameters, and the structural features of connection relationships.
[0016] The timestamp alignment submodule synchronizes data from different sources according to timestamps, ensuring the temporal consistency of feature vectors.
[0017] Preferably, the large model service module includes:
[0018] The feature semantic encoding submodule converts structured features into semantic vectors and captures the relationships between features through an attention mechanism;
[0019] The context modeling submodule maintains the historical state of the training process and combines the current feature vector to predict future trends.
[0020] The parameter tuning instruction generation submodule generates parameter adjustment suggestions based on semantic encoding results, and supports output in natural language or structured format.
[0021] Preferably, the reinforcement learning hyperparameter tuning module includes: an action space design submodule, which defines a set of hyperparameter tuning actions, including the adjustment range of continuous parameters and options for discrete parameters; a state representation submodule, which encodes log features, environmental states, and model parameters into state vectors; a reward function design submodule, which designs reward signals based on model performance indicators to guide the agent to learn the optimal hyperparameter tuning strategy; and a policy optimization submodule, which trains the hyperparameter tuning agent in a simulated environment using PPO or SAC algorithms, enabling it to select the optimal hyperparameter tuning action based on the state vector.
[0022] The feedback optimization module includes: a performance evaluation submodule, which evaluates the performance of the model after parameter tuning on the validation set and calculates key metrics; an error backpropagation submodule, which uses the performance evaluation results as feedback signals to update the large model parameters and parameter tuning proxy strategies through gradient descent or reinforcement learning strategies; and an adaptive adjustment submodule, which dynamically adjusts the feedback weights according to the training phase to balance short-term performance with long-term exploration needs.
[0023] Preferably, the system also includes a multimodal log fusion and parsing unit, which is used to extract features from scalar, histogram, and image multimodal data through CNN and LSTM fusion, and generate a fused feature vector as input to the large model service module;
[0024] It also includes a dynamic parameter tuning strategy generation unit, which is based on a context-aware dynamic parameter tuning algorithm that automatically adjusts the frequency and magnitude of parameter tuning according to the training stage, environmental complexity, and model state.
[0025] An automated method for tuning reinforcement learning training parameters based on TensorBoard log-driven large model services includes the following steps:
[0026] Data Acquisition: The data acquisition module collects TensorBoard logs, environmental interaction data, and model parameter snapshots in real time during the reinforcement learning training process;
[0027] Log parsing: The log parsing module performs structured parsing on the collected logs, extracting scalar metrics, histogram statistics, model structure information, and event timestamps to construct multidimensional feature vectors;
[0028] Large Model Service: Leveraging the large model service module, semantic encoding and context modeling of feature vectors are performed based on pre-trained language models to generate parameter adjustment suggestions;
[0029] Reinforcement learning parameter tuning: Through the reinforcement learning parameter tuning module, parameter adjustment suggestions are transformed into specific parameter tuning actions, and the parameter tuning strategy is optimized by using reinforcement learning algorithms;
[0030] Feedback optimization: The feedback optimization module is used to backpropagate errors based on model performance evaluation results and dynamically update the parameters of the large model and the parameter tuning proxy strategy.
[0031] Preferably, the log parsing step specifically includes:
[0032] Scalar metric extraction: Read scalar data such as loss function value, accuracy, and learning rate from the logs and store them as a multidimensional array in time series order;
[0033] Histogram analysis: Analyze histogram data of weights, gradients, and activation values, and calculate the mean, variance, and skewness statistics;
[0034] Model structure analysis: Extracting features such as the number of model layers, the number of parameters, and the structural features of connection relationships;
[0035] Timestamp alignment: Synchronize data from different sources according to timestamps to ensure the temporal consistency of feature vectors.
[0036] Preferably, the large model service steps specifically include:
[0037] Feature semantic encoding: converting structured features into semantic vectors and capturing the relationships between features through an attention mechanism;
[0038] Context modeling: Maintaining the historical state of the training process and combining the current feature vectors to predict future trends;
[0039] Parameter tuning instruction generation: Generates parameter adjustment suggestions based on semantic encoding results, supporting output in natural language or structured format.
[0040] Preferably, the reinforcement learning parameter tuning steps specifically include: action space design: defining a set of parameter tuning actions, including the adjustment range of continuous parameters and options for discrete parameters; state representation: encoding log features, environmental states, and model parameters into state vectors; reward function design: designing reward signals based on model performance indicators to guide the agent to learn the optimal parameter tuning strategy; policy optimization: training the parameter tuning agent in a simulated environment using PPO or SAC algorithms, enabling it to select the optimal parameter tuning action based on the state vector;
[0041] The feedback optimization steps specifically include: performance evaluation: evaluating the performance of the model after parameter tuning on the validation set and calculating key indicators; error backpropagation: using the performance evaluation results as feedback signals to update the large model parameters and parameter tuning proxy strategies through gradient descent or reinforcement learning strategies; adaptive adjustment: dynamically adjusting the feedback weights according to the training phase to balance short-term performance and long-term exploration needs.
[0042] Preferably, the following steps are also included:
[0043] Multimodal log fusion parsing: Scalar, histogram, and image multimodal data are fused using CNN and LSTM to extract features and generate a fused feature vector as input to the large model service module;
[0044] Dynamic parameter tuning strategy generation: A context-aware dynamic parameter tuning algorithm that automatically adjusts the frequency and magnitude of parameter tuning based on the training stage, environmental complexity, and model state.
[0045] Compared with the prior art, the beneficial effects of the present invention are:
[0046] This invention proposes an automated parameter tuning system and method for reinforcement learning training based on TensorBoard log-driven and large model services, which improves parameter tuning efficiency: by reducing manual intervention through automated parameter tuning, experiments show that in MuJoCo continuous control tasks, the parameter tuning time of this invention is reduced from 72 hours of traditional methods to 24 hours, with an efficiency improvement of 3 times.
[0047] Optimize model performance: The dynamic parameter tuning strategy can adapt to the non-stationarity during the training process. In the training of humanoid robot motion control models, the average model reward is increased by 15%-20%, and the convergence speed is accelerated by 30%.
[0048] Reduced R&D costs: Automated parameter tuning reduces reliance on senior engineers. It is estimated that a single reinforcement learning project can save more than 30% in human resources costs.
[0049] Furthermore, by implementing this invention, the technical barriers to reinforcement learning can be lowered, enabling SMEs and research institutions to apply advanced AI technologies at a lower cost, thereby promoting industrial innovation and fair competition. It also facilitates the expansion of the technology from fields such as gaming and robotics to high-value scenarios such as healthcare and finance. Attached Figure Description
[0050] Figure 1 This is a system block diagram of the present invention. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0052] Example 1: This invention provides a technical solution: an automated tuning system for reinforcement learning training parameters based on TensorBoard log-driven and large model services. The system includes a data acquisition layer, a log parsing layer, a large model service layer, a reinforcement learning parameter tuning layer, and a feedback optimization layer. Each layer works collaboratively to achieve closed-loop optimization.
[0053] The data acquisition layer is the foundation of the entire reinforcement learning hyperparameter tuning system, bearing the crucial task of collecting various types of raw data during reinforcement learning training. These data sources are extensive, including not only TensorBoard training logs, which record detailed events and metrics during training, but also environmental interaction data. For example, state information reflects the specific circumstances of the model's environment, action information reflects the model's behavior in different states, and reward information provides feedback on the model's actions. Simultaneously, model parameter snapshots are also included in the collection, recording the specific parameter settings of the model at different training stages. This layer boasts strong compatibility, supporting multiple deep learning frameworks such as TensorFlow and PyTorch. Through hook functions or callback mechanisms, training events can be captured in real time, accurately recording even the slightest changes during training, thus ensuring data integrity and accuracy and providing a reliable basis for subsequent analysis and processing.
[0054] The log parsing layer performs deep structured analysis on the collected TensorBoard logs, aiming to extract key metrics and construct feature vectors. During parsing, scalar metrics are extracted first, precisely reading scalar data such as loss function values, accuracy, and learning rate from the logs and storing them as multi-dimensional arrays in time series order. This allows for clear observation of the trends of these metrics as the training process progresses. Next, histogram and distribution analysis are performed, meticulously analyzing the histogram data of weights, gradients, and activation values, calculating statistics such as mean, variance, and skewness. These statistics capture changes in parameter distribution, thereby understanding the stability of the model during training. Simultaneously, model structure information is extracted, including structural features such as the number of model layers, number of parameters, and connection relationships. This information provides crucial contextual information for subsequent parameter tuning strategies. Finally, event timestamp alignment is performed, synchronizing data from different sources, such as logs and environmental interaction data, according to timestamps to ensure the temporal consistency of feature vectors, enabling subsequent analysis to be based on a unified time benchmark.
[0055] The large model service layer, based on pre-trained language models such as GPT-4 and LLaMA, builds a powerful semantic understanding and inference engine. This layer has several key functions, including feature semantic encoding, which converts the structured features output by the log parsing layer into semantic vectors and uses an attention mechanism to capture the relationships between features. For example, by combining the loss function value with the gradient distribution, it infers the model's convergence state, providing deeper insights for hyperparameter tuning. The context modeling function is dedicated to maintaining the historical state of the training process and predicting future trends by combining the current feature vectors. For example, based on the reward changes over the previous N epochs, it predicts the impact of the current hyperparameter tuning strategy on the model's long-term performance, making hyperparameter tuning more forward-looking. The hyperparameter tuning instruction generation function generates specific parameter adjustment suggestions based on the semantic encoding results, such as "reduce the learning rate by 10%" and "increase the exploration rate by 0.05", and outputs them in natural language or structured format for easy subsequent hyperparameter tuning operations.
[0056] The reinforcement learning hyperparameter tuning layer transforms the output of the large model service layer into specific hyperparameter tuning actions and continuously optimizes the tuning strategy through reinforcement learning algorithms. Its core components include action space design, which clearly defines the set of tuning actions, covering the adjustment range of continuous parameters (such as learning rate and discount factor) and options for discrete parameters (such as optimizer type), providing a clear direction for hyperparameter tuning. State representation encodes log features, environmental states, and model parameters into state vectors, serving as input to the reinforcement learning agent, enabling the agent to fully perceive the training state. Reward function design, based on model performance metrics such as average reward and convergence speed, designs a reasonable reward signal to guide the agent in learning the optimal hyperparameter tuning strategy. The policy optimization algorithm employs advanced algorithms such as PPO or SAC, training the hyperparameter tuning agent in a simulated environment, enabling it to select the optimal tuning action based on the state vector, improving tuning efficiency and accuracy.
[0057] The feedback optimization layer continuously optimizes the large model service layer and the reinforcement learning hyperparameter tuning layer through a closed-loop feedback mechanism. The specific process includes performance evaluation, which comprehensively evaluates the performance of the tuned model on the validation set, calculating key metrics such as accuracy and F1 score to accurately understand the model's performance. Error backpropagation uses the performance evaluation results as feedback signals, updating the large model parameters and hyperparameter tuning surrogate strategies through gradient descent or reinforcement learning strategies, allowing the model and tuning strategies to continuously adapt to training requirements. Adaptive adjustment dynamically adjusts the feedback weights according to the training phase, such as the exploration phase and the convergence phase. During the exploration phase, the focus is on trying new hyperparameter tuning strategies to find better solutions; during the convergence phase, the focus is on stability and performance improvement, balancing short-term performance with long-term exploration needs to ensure the entire system always evolves towards the optimal direction.
[0058] Example 2, based on Example 1, proposes an automated tuning device for reinforcement learning training parameters based on TensorBoard log-driven and large model services, including:
[0059] The log acquisition card connects to the training node via a PCIe interface and transmits log data in real time.
[0060] The data processing server runs log parsing and feature extraction algorithms; the log parsing engine adopts a microservice architecture design, supporting horizontal scaling and fault isolation; the feature storage module uses a time-series database (such as InfluxDB) to store multi-dimensional feature vectors; the task scheduler dynamically allocates computing resources according to the load.
[0061] The large model inference accelerator card deploys a pre-trained language model to achieve low-latency semantic inference. The large model inference accelerator card integrates the TensorRT optimization engine, supports FP16 and INT8 quantization inference, and has an inference latency of less than 10ms. The parameter tuning execution unit includes a parameter cache, an instruction parser, and a gradient update module to achieve sub-millisecond parameter adjustment.
[0062] The hyperparameter tuning execution unit interacts with the training framework through the API interface to dynamically adjust hyperparameters;
[0063] The hardware units are interconnected via a high-speed bus, supporting parallel computing and low-latency communication.
[0064] Example 3, based on Example 1, proposes an automated tuning method for reinforcement learning training parameters based on TensorBoard log-driven and large model services, including the following steps:
[0065] Data Acquisition: The data acquisition module collects TensorBoard logs, environmental interaction data, and model parameter snapshots in real time during the reinforcement learning training process.
[0066] Log parsing: The log parsing module performs structured analysis on the collected logs, extracting scalar metrics, histogram statistics, model structure information, and event timestamps to construct multidimensional feature vectors. Specifically, this includes: Scalar metric extraction: Reading loss function values, accuracy, and learning rate scalar data from the logs and storing them as multidimensional arrays in time series order; Histogram analysis: Parsing histogram data of weights, gradients, and activation values, calculating mean, variance, and skewness statistics; Model structure analysis: Extracting the number of model layers, number of parameters, and connection structure features; Timestamp alignment: Synchronizing data from different sources according to timestamps to ensure temporal consistency of feature vectors.
[0067] Large Model Service: Leveraging the large model service module, semantic encoding and context modeling are performed on feature vectors based on pre-trained language models to generate parameter adjustment suggestions; specifically including: Feature Semantic Encoding: Converting structured features into semantic vectors and capturing the relationships between features through an attention mechanism; Context Modeling: Maintaining the historical state of the training process and predicting future trends by combining the current feature vectors; Parameter Adjustment Instruction Generation: Generating parameter adjustment suggestions based on the semantic encoding results, supporting output in natural language or structured formats.
[0068] Reinforcement Learning Parameter Tuning: The reinforcement learning parameter tuning module translates parameter adjustment suggestions into specific tuning actions, and optimizes the tuning strategy using reinforcement learning algorithms. Specifically, this includes: Action Space Design: Defining a set of tuning actions, including the adjustment range for continuous parameters and options for discrete parameters; State Representation: Encoding log features, environment states, and model parameters into state vectors; Reward Function Design: Designing reward signals based on model performance metrics to guide the agent in learning the optimal tuning strategy; Policy Optimization: Training the parameter tuning agent in a simulated environment using PPO or SAC algorithms, enabling it to select the optimal tuning action based on the state vector.
[0069] Feedback optimization: A feedback optimization module is used to backpropagate errors based on model performance evaluation results and dynamically update the large model parameters and parameter tuning surrogate strategies. Specifically, this includes: Performance evaluation: Evaluating the performance of the tuned model on the validation set and calculating key metrics; Error backpropagation: Using the performance evaluation results as feedback signals, updating the large model parameters and parameter tuning surrogate strategies through gradient descent or reinforcement learning strategies; Adaptive adjustment: Dynamically adjusting feedback weights according to the training phase to balance short-term performance with long-term exploration needs.
[0070] It also includes the following steps:
[0071] Multimodal log fusion parsing: Scalar, histogram, and image multimodal data are fused using CNN and LSTM to extract features and generate a fused feature vector as input to the large model service module;
[0072] Dynamic parameter tuning strategy generation: A context-aware dynamic parameter tuning algorithm that automatically adjusts the frequency and magnitude of parameter tuning based on the training stage, environmental complexity, and model state.
[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An automated parameter tuning system for reinforcement learning training based on TensorBoard log-driven and large model services, characterized in that: The system includes: The data acquisition module is used to collect TensorBoard logs, environmental interaction data, and model parameter snapshots in real time during the reinforcement learning training process. The log parsing module performs structured parsing on the collected logs, extracting scalar metrics, histogram statistics, model structure information, and event timestamps to construct multidimensional feature vectors. The large model service module performs semantic encoding and context modeling on feature vectors based on a pre-trained language model, and generates parameter adjustment suggestions. The reinforcement learning parameter tuning module translates parameter adjustment suggestions into specific parameter tuning actions and optimizes the parameter tuning strategy through reinforcement learning algorithms. The feedback optimization module backpropagates the error based on the model performance evaluation results and dynamically updates the parameters of the large model and the parameter tuning proxy strategy. The log parsing module includes: The scalar metric extraction submodule reads scalar data such as loss function value, accuracy, and learning rate from the logs and stores them as a multidimensional array in time series order. The histogram analysis submodule parses histogram data of weights, gradients, and activation values, and calculates the mean, variance, and skewness statistics. The model structure analysis submodule extracts features such as the number of model layers, the number of parameters, and the structural features of connection relationships. The timestamp alignment submodule synchronizes data from different sources according to timestamps, ensuring the temporal consistency of feature vectors. The large model service module includes: The feature semantic encoding submodule converts structured features into semantic vectors and captures the relationships between features through an attention mechanism; The context modeling submodule maintains the historical state of the training process and combines the current feature vector to predict future trends. The parameter tuning instruction generation submodule generates parameter adjustment suggestions based on semantic encoding results, and supports output in natural language or structured format. The reinforcement learning hyperparameter tuning module includes: an action space design submodule, which defines a set of hyperparameter tuning actions, including the adjustment range of continuous parameters and options for discrete parameters; a state representation submodule, which encodes log features, environmental states, and model parameters into state vectors; a reward function design submodule, which designs reward signals based on model performance metrics to guide the agent in learning the optimal hyperparameter tuning strategy; and a policy optimization submodule, which trains the hyperparameter tuning agent in a simulated environment using PPO or SAC algorithms, enabling it to select the optimal hyperparameter tuning action based on the state vector. The feedback optimization module includes: a performance evaluation submodule, which evaluates the performance of the model after parameter tuning on the validation set and calculates key metrics; an error backpropagation submodule, which uses the performance evaluation results as feedback signals to update the large model parameters and parameter tuning proxy strategies through gradient descent or reinforcement learning strategies; and an adaptive adjustment submodule, which dynamically adjusts the feedback weights according to the training phase to balance short-term performance with long-term exploration needs. The system also includes a multimodal log fusion and parsing unit, which is used to extract features from scalar, histogram, and image multimodal data through CNN and LSTM fusion, and generate fused feature vectors as input to the large model service module; It also includes a dynamic parameter tuning strategy generation unit, which is based on a context-aware dynamic parameter tuning algorithm that automatically adjusts the frequency and magnitude of parameter tuning according to the training stage, environmental complexity, and model state.
2. A method for automatically tuning reinforcement learning training parameters based on TensorBoard log-driven and large model services, applying the system described in claim 1, characterized in that: Includes the following steps: Data Acquisition: The data acquisition module collects TensorBoard logs, environmental interaction data, and model parameter snapshots in real time during the reinforcement learning training process; Log parsing: The log parsing module performs structured parsing on the collected logs, extracting scalar metrics, histogram statistics, model structure information, and event timestamps to construct multidimensional feature vectors; Large Model Service: Leveraging the large model service module, semantic encoding and context modeling of feature vectors are performed based on pre-trained language models to generate parameter adjustment suggestions; Reinforcement learning parameter tuning: Through the reinforcement learning parameter tuning module, parameter adjustment suggestions are transformed into specific parameter tuning actions, and the parameter tuning strategy is optimized by using reinforcement learning algorithms; Feedback optimization: The feedback optimization module is used to backpropagate errors based on model performance evaluation results and dynamically update the parameters of the large model and the parameter tuning proxy strategy. The log parsing steps specifically include: Scalar metric extraction: Read scalar data such as loss function value, accuracy, and learning rate from the logs and store them as a multidimensional array in time series order; Histogram analysis: Analyze histogram data of weights, gradients, and activation values, and calculate the mean, variance, and skewness statistics; Model structure analysis: Extracting features such as the number of model layers, the number of parameters, and the structural features of connection relationships; Timestamp alignment: Synchronize data from different sources according to timestamps to ensure the temporal consistency of feature vectors; The large model service steps specifically include: Feature semantic encoding: converting structured features into semantic vectors and capturing the relationships between features through an attention mechanism; Context modeling: Maintaining the historical state of the training process and combining the current feature vectors to predict future trends; Parameter tuning instruction generation: Generate parameter adjustment suggestions based on semantic encoding results, supporting output in natural language or structured format; The reinforcement learning parameter tuning steps specifically include: Action space design: defining a set of parameter tuning actions, including the adjustment range of continuous parameters and the options for discrete parameters; State representation: encoding log features, environmental states, and model parameters into state vectors; Reward function design: designing reward signals based on model performance metrics to guide the agent to learn the optimal parameter tuning strategy; Policy optimization: training the parameter tuning agent in a simulated environment using PPO or SAC algorithms, enabling it to select the optimal parameter tuning action based on the state vector; The feedback optimization steps specifically include: performance evaluation: evaluating the performance of the model after parameter tuning on the validation set and calculating key indicators; error backpropagation: using the performance evaluation results as feedback signals to update the large model parameters and parameter tuning proxy strategies through gradient descent or reinforcement learning strategies; adaptive adjustment: dynamically adjusting the feedback weights according to the training phase to balance short-term performance and long-term exploration needs. It also includes the following steps: Multimodal log fusion parsing: Scalar, histogram, and image multimodal data are fused using CNN and LSTM to extract features and generate a fused feature vector as input to the large model service module; Dynamic parameter tuning strategy generation: A context-aware dynamic parameter tuning algorithm that automatically adjusts the frequency and magnitude of parameter tuning based on the training stage, environmental complexity, and model state.