Summary of Integrating Large Language Models and Reinforcement Learning For Non-linear Reasoning, by Yoav Alon and Cristina David
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning
by Yoav Alon, Cristina David
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel architecture is proposed to address the limitation of Large Language Models (LLMs) in long-term planning by introducing a Reinforcement Learning (RL) Agent that guides LLM’s space exploration. The RL Agent has access to domain-specific information, allowing it to make decisions based on specific metrics, which were not considered in the LLM’s training objective. This approach enables non-linear reasoning through alternative paths and backtracking. The architecture is evaluated on the program equivalence task, comparing its performance against Chain of Thought (CoT) and Tree of Thoughts (ToT). Both downstream task accuracy and intermediate reasoning steps are assessed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models struggle with long-term planning. A new idea combines Reinforcement Learning with these models to help them explore ideas better. Imagine a guide who helps the model decide what’s good or bad based on specific rules. This allows the model to focus on small steps, without having to plan far ahead. The approach is tested on a problem where you have to figure out if two programs do the same thing. It works well and is even better than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
