Summary of Imitate, Explore, and Self-improve: a Reproduction Report on Slow-thinking Reasoning Systems, by Yingqian Min et al.
Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
by Yingqian Min, Zhipeng Chen, Jinhao Jiang, Jie Chen, Jia Deng, Yiwen Hu, Yiru Tang, Jiapeng Wang, Xiaoxue Cheng, Huatong Song, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Ji-Rong Wen
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed “imitate, explore, and self-improve” framework, denoted as STILL-2, is a technical approach to train reasoning models like o1, which have shown remarkable capabilities in solving complex tasks. The framework involves fine-tuning the model using distilled long-form thought data, enabling it to invoke a slow-thinking mode. The model then explores challenging problems by generating multiple rollouts and iteratively refines its training dataset through self-improvement. Experimental results on three benchmarks demonstrate competitive performance compared to industry-level reasoning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents an approach to train reasoning models like o1, which can solve complex tasks. It uses a framework that involves fine-tuning the model using special data, allowing it to think slowly and carefully. The model then tries different solutions and improves its training by learning from its mistakes. This helps the model get better at solving problems. The results show that this approach is as good as what industry experts have developed. |
Keywords
» Artificial intelligence » Fine tuning
