Summary of Reinforcement Learning From Reflective Feedback (rlrf): Aligning and Improving Llms Via Fine-grained Self-reflection, by Kyungjae Lee et al.
Reinforcement Learning from Reflective Feedback (RLRF): Aligning and Improving LLMs via Fine-Grained Self-Reflection
by Kyungjae Lee, Dasol Hwang, Sunghyun Park, Youngsoo Jang, Moontae Lee
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Reinforcement Learning from Reflective Feedback (RLRF) framework aims to overcome limitations in current Large Language Models (LLMs) by leveraging fine-grained feedback based on detailed criteria. This novel approach employs a self-reflection mechanism to systematically explore and refine LLM responses, followed by fine-tuning via a reinforcement learning algorithm along with promising responses. The RLRF framework is evaluated across three tasks: Just-Eval, Factuality, and Mathematical Reasoning, demonstrating its efficacy in improving the core capabilities of LLMs beyond superficial surface-level adjustments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF often prioritizes stylistic changes over improving downstream performance of LLMs. To improve LLM responses, a new framework is proposed that uses fine-grained feedback based on detailed criteria to refine LLM capabilities. This approach helps to systematically explore and refine responses using a self-reflection mechanism. The results show that this method can be effective in making significant improvements. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Rlhf
