Summary of Training Language Models to Self-correct Via Reinforcement Learning, by Aviral Kumar et al.
Training Language Models to Self-Correct via Reinforcement Learning
by Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, Aleksandra Faust
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 authors develop a multi-turn online reinforcement learning (RL) approach, SCoRe, to improve large language models’ (LLMs) self-correction ability using entirely self-generated data. The approach addresses shortcomings in current methods by training under the model’s own distribution of self-generated correction traces and applying regularization to steer the learning process into learning effective self-correction behavior. The authors demonstrate state-of-the-art self-correction performance with Gemini 1.0 Pro and 1.5 Flash models, achieving improvements of 15.6% and 9.1%, respectively, on MATH and HumanEval tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to be able to correct their own mistakes, but current methods aren’t very good at this. Researchers created a new approach called SCoRe that helps LLMs learn to self-correct by training them with their own mistakes. They found that previous methods didn’t work well because they were using the wrong kind of data or relied too much on one type of correction behavior. The new method addresses these issues and is able to significantly improve an LLM’s ability to correct its own mistakes. |
Keywords
» Artificial intelligence » Gemini » Regularization » Reinforcement learning
