# Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning

**Authors:** Shelly Bensal, Umar Jamil, Christopher Bryant, Melisa Russak, Kiran Kamble, Dmytro Mozolevskyi, Muayad Ali, Waseem AlShikh  
**arXiv:** [2505.24726](https://arxiv.org/abs/2505.24726)  
**Date:** 2025-05-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2505.24726v1/

## Key points

- Novel approach to improving performance of large language models (LLMs) through self-reflection and reinforcement learning mechanisms
- Methodology involves prompting model to generate self-reflective commentaries upon failing a task, analyzing previous attempt, and making a second attempt with insights gained
- Use of reinforcement learning techniques to reward tokens generated during self-reflection phase for more effective reflections in future attempts
- Enables LLMs to improve performance on diverse tasks without requiring task-specific training data
- Experimental evaluations show significant performance gains across various model architectures, with smaller fine-tuned models outperforming larger models in some cases
- Framework leverages self-reflection and reinforcement learning in a task-agnostic manner with binary feedback signals for developing more reliable and adaptable language models that can autonomously improve on challenging tasks

## Summary

This paper presents a novel approach to improving the performance of large language models (LLMs) by incorporating self-reflection and reinforcement learning mechanisms. While LLMs have shown remarkable proficiency in various natural language processing tasks, they still face challenges in certain domains where accurate responses may be difficult to provide. Traditional methods of retraining or fine-tuning on specific datasets may not always be feasible or practical. Our proposed methodology involves prompting the model to generate self-reflective commentaries upon failing a task, analyzing its previous attempt, and then making a second attempt with insights gained from the reflection. If successful on the subsequent try, we employ reinforcement learning techniques to reward the tokens generated during the self-reflection phase, encouraging more effective reflections in future attempts. This process enables LLMs to improve their performance on diverse tasks without requiring task-specific training data. Through experimental evaluations on tasks such as APIGen function calling and Countdown equation solving, we demonstrate significant performance gains across various model architectures. Notably, even smaller fine-tuned models outperform larger models in some cases, showcasing the effectiveness of our approach in enhancing LLM capabilities. By leveraging self-reflection and reinforcement learning in a task-agnostic manner with only binary feedback signals, our framework offers a promising pathway towards developing more reliable and adaptable language models that can autonomously improve on challenging tasks.