Autonomous Machine Training via LLM Reward Function Generation
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Solution Overview
Problem
Current methods for training autonomous machines to perform tasks using machine learning require human demonstrations or handcrafted trajectories, making them inefficient for scaling, particularly in aligning textual descriptions of tasks with computational goals and reward functions.
Innovation Solution
The method involves generating a prompt by combining natural language descriptions of tasks and environments, using a large language model to create a reward or goal position function, and training a policy for the autonomous machine with a state description, allowing for automated alignment of textual descriptions with computational objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human demonstrations or handcrafted trajectories are used for training autonomous machines, then the accuracy of task execution is improved, but the scalability of the training method deteriorates
Solution Approach 1:
The patent uses large language models to generate synthetic training data that copies and extends the patterns from limited human demonstrations. The LLM learns from a small set of human-labeled examples and generates numerous synthetic training pairs, effectively copying the essence of human expertise while scaling the training data volume significantly.
Solution Approach 2:
The system performs preliminary training of a large language model on a small subset of human demonstrations to create a distilled policy. This preliminary action enables the LLM to later generate synthetic training data autonomously, preparing the foundation for scalable training without requiring continuous human input.
2Manufacturing precision
If textual descriptions of tasks are aligned with computational goals and reward functions manually, then the accuracy of policy learning is improved, but the complexity and time required for training increases
Solution Approach 1:
The patent replaces manual mechanical alignment processes with an automated large language model system. The LLM processes natural language task descriptions and automatically generates corresponding computational goals and reward functions, substituting human expert judgment with an automated intelligent system that handles the complex alignment task.
Solution Approach 2:
The large language model serves as an intermediary between natural language task descriptions and computational reward functions. It translates the high-level textual goals into executable computational objectives, mediating the gap between human intentions and machine execution without requiring direct manual alignment.
3Manufacturing precision
If more training data is generated to improve policy learning, then the accuracy of autonomous machine performance is improved, but the time and computational resources required increases
Solution Approach 1:
The system uses a large language model to copy and generate synthetic training data that mirrors real task scenarios. By copying the structure and patterns from limited real demonstrations and generating variations through the LLM, the system expands the training data volume without requiring proportional increases in time-consuming data collection efforts.
Solution Approach 2:
The patent performs preliminary training of the large language model on a small set of real demonstrations to create a distilled policy. This preliminary action compresses the essential learning into the LLM, which then可以快速 generate synthetic training data without requiring extensive real-world data collection or processing time.
Data Source
AI summary
Computer-implemented methods are included for training an autonomous machine to perform a target operation in a target environment. The methods include receiving a natural language description of the target operation and a natural language description of the target environment. The methods further include generating a prompt such as a reward and/or goal position signature by combining the natural language description of a target task or goal and the natural language description of the target environment. The methods then generate a reward or goal position function by prompting a large language model with the generated prompt. The methods further include computing a state description using a model of the target environment, and training a policy for the autonomous machine to perform the target task or goal using the generated function and state description.