Summary of Reflect-rl: Two-player Online Rl Fine-tuning For Lms, by Runlong Zhou et al.
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
by Runlong Zhou, Simon S. Du, Beibin Li
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers propose a novel approach to fine-tuning language models (LMs) for multi-round interactions. The method, called Reflect-RL, combines supervised fine-tuning (SFT) and online reinforcement learning (RL) in an interactive decision-making environment. A frozen reflection model assists the policy model, which is trained using negative example generation and curriculum learning. The authors verify that Reflect-RL outperforms SFT and online RL without reflection, achieving state-of-the-art performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are getting smarter! Scientists have developed a new way to improve language models so they can work better in situations where people need to interact with each other multiple times. This is important because it means we can use these models in more real-life situations, like having conversations or making decisions together. The new method, called Reflect-RL, helps the model learn from its mistakes and make better choices by working with a “reflection” model that keeps track of what’s not working. By using this approach, scientists were able to fine-tune language models in a way that outperforms other methods. |
Keywords
* Artificial intelligence * Curriculum learning * Fine tuning * Reinforcement learning * Supervised
