Summary of Recursive Introspection: Teaching Language Model Agents How to Self-improve, by Yuxiao Qu et al.
Recursive Introspection: Teaching Language Model Agents How to Self-Improve
by Yuxiao Qu, Tianjun Zhang, Naman Garg, Aviral Kumar
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper introduces RISE (Recursive IntroSpEction), an approach to fine-tuning large language models (LLMs) to improve their ability to learn from mistakes and adapt to challenging prompts. Building upon previous work, the authors demonstrate that even strong proprietary LLMs can benefit from recursive introspection, which enables them to correct errors and refine their responses over multiple turns. The proposed method is based on online imitation learning and reinforcement learning principles, utilizing multi-turn data collection and training strategies to imbue the model with self-corrective capabilities. Experimental results show that RISE enhances the performance of Llama2, Llama3, and Mistral models on math reasoning tasks, outperforming single-turn strategies while maintaining one-turn abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to improve language models’ ability to learn from their mistakes. The authors show that even very strong language models can benefit from being able to reflect on their own actions and correct errors as they go. They propose an approach called RISE, which helps language models adapt to challenging prompts by allowing them to refine their responses over multiple attempts. The results of the study demonstrate that this new approach leads to better performance on math reasoning tasks. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning
