Offline Skill Distillation for Long-Horizon LLM Decision-Making
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Solution Overview
Problem
Large language models (LLMs) face challenges in generating high-quality solutions for complex and long-horizon tasks due to limited context length and the need for extensive fine-tuning, especially in heterogeneous real-world environments, leading to resource-intensive online task-specific reasoning and action.
Innovation Solution
An offline data-driven method that segments skills from interaction logs, abstracts reusable functions, and generates distilled tips to augment the LLM, allowing it to complete tasks without fine-tuning, using both text-based and code-based policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If few-shot examples are provided to LLM for in-context learning, then the LLM can perform one-step text generation tasks, but the examples are insufficient for complex and long-horizon tasks requiring sequential decision-making
Solution Approach 1:
The system performs preliminary actions by pre-processing offline interaction data into skill-oriented sub-trajectories and distilling task-specific knowledge before the LLM needs to solve complex tasks. This preparation work creates structured training materials that enable the LLM to handle sequential decision-making without extensive fine-tuning.
Solution Approach 2:
The system introduces an intermediary processing layer that transforms raw interaction data into skill-oriented sub-trajectories and distilled tips. This intermediary structure bridges the gap between simple few-shot examples and complex sequential tasks, enabling the LLM to leverage offline data effectively without direct fine-tuning.
2Reliability
If large amount of data or demonstrations are provided to improve task understanding, then the LLM can better understand environment dynamics, but the context length of LLM is limited
Solution Approach 1:
The system segments the context by dividing interaction data into skill-oriented sub-trajectories focused on specific skills. This segmentation allows the LLM to process relevant information in manageable chunks rather than being overwhelmed by the full context, effectively bypassing the context length limitation while maintaining reliability.
Solution Approach 2:
The system extracts only the most relevant task-specific information from offline data through distillation, creating condensed tips and knowledge representations. This extraction process removes unnecessary information while retaining critical insights about environment dynamics, fitting within the LLM's context window.
3Manufacturing precision
If extensive finetuning is performed to improve LLM performance on specific tasks, then task-specific accuracy improves, but technical resources such as CPU, memory and other resources are heavily consumed
Solution Approach 1:
Instead of modifying the LLM through resource-intensive fine-tuning, the system creates copies of task-specific knowledge in the form of distilled tips and skill-oriented sub-trajectories. These copied knowledge representations can be injected into the LLM's context without altering the model weights, achieving task-specific accuracy with minimal computational resource consumption.
Solution Approach 2:
The system changes the parameters of the input data rather than the model itself. By transforming offline interaction data into skill-oriented sub-trajectories and distilled tips, the system modifies the input distribution to match task requirements, achieving adaptation without the computational cost of fine-tuning model parameters.
Data Source
AI summary
A method and system for performing a task requiring a plurality of sequential decision-making operations by an autonomous agent are disclosed. The method includes acquiring a portion of offline data from a database, and discovering a set of separate skills. The method further includes segmenting the offline data and segmenting the offline data according to skill, and abstracting the segmented offline data into reusable functions. From the abstracted reusable functions, distilling primitives for each skill and distilling reusable tips in various functions. Modifying skill based text-based and code-based policies for augmenting the autonomous agent, and processing a task requiring a set of sequential decision-making operations using the augmented autonomous agents.