Decision Model Meta-Parameter Training via Perturbation
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Solution Overview
Problem
The high cost and requirement for high-quality training data in secondary training of AI models make it impractical for ordinary users to perform model training, limiting the efficiency and accessibility of model adaptation for specific tasks.
Innovation Solution
The method involves training a decision-making model in a primary environment using perturbation parameters and observation data, allowing the model to adapt without pre-prepared training data, and then applying the learned meta-parameters to a secondary environment for task-specific decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If secondary training is performed with high-quality training data, then model accuracy for specific tasks is improved, but training cost and data preparation complexity increase
Solution Approach 1:
The patent performs pre-training on a large-scale dataset to obtain an initial model before secondary training. This preliminary action enables the model to learn general features and patterns, reducing the need for extensive high-quality task-specific training data while maintaining accuracy.
Solution Approach 2:
The patent uses data augmentation techniques to generate synthetic training data that mimics real-world data distributions. This copying approach creates additional training samples without requiring manual data collection and annotation, reducing data preparation complexity while maintaining model accuracy.
2Measurement precision
If secondary training is performed with high-quality training data, then model accuracy for specific tasks is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs pre-training on a large-scale dataset to obtain an initial model before secondary training. This preliminary action enables the model to learn general features and patterns, reducing the need for extensive high-quality task-specific training data while maintaining accuracy.
Solution Approach 2:
The patent adjusts training parameters such as learning rate, batch size, and number of epochs based on the pre-trained model's performance. This dynamic parameter adjustment optimizes the secondary training process, reducing training time while maintaining or improving model accuracy for specific tasks.
3Adaptability or versatility
If traditional secondary training is performed, then model specialization for specific tasks is achieved, but user accessibility and ease of operation decrease
Solution Approach 1:
The patent implements automated model selection and training parameter optimization that requires minimal user intervention. The system automatically selects appropriate pre-trained models and adjusts training parameters based on the task requirements, enabling ordinary users to perform model adaptation without expert knowledge.
Solution Approach 2:
The patent creates a unified training framework that can adapt multiple pre-trained models to various tasks using the same secondary training process. This universal approach allows a single system to serve multiple functions and task types, improving user accessibility while maintaining model specialization capabilities.
Data Source
AI summary
A method for training a decision-making model parameter, a decision determination method, an electronic device, and a non-transitory computer-readable storage medium are provided. In the method, a perturbation parameter is generated according to a meta-parameter, and first observation information of a primary training environment is acquired based on the perturbation parameter. According to the first observation information, an evaluation parameter of the perturbation parameter is determined. According to the perturbation parameter and the evaluation parameter thereof, an updated meta-parameter is generated. The updated meta-parameter is determined as a target meta-parameter, when it is determined, according to the meta-parameter and the updated meta-parameter, that a condition for stopping primary training is met. According to the target meta-parameter, a target memory parameter corresponding to a secondary training task is determined, where the target memory parameter and the target meta-parameter are used to make a decision corresponding to a prediction task.