Summary of Openrft: Adapting Reasoning Foundation Model For Domain-specific Tasks with Reinforcement Fine-tuning, by Yuxiang Zhang et al.
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
by Yuxiang Zhang, Yuqi Yang, Jiangming Shu, Yuhang Wang, Jinlin Xiao, Jitao Sang
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 paper introduces OpenRFT, a method for fine-tuning generalist reasoning models for domain-specific tasks, building upon Reinforcement Fine-Tuning (RFT). This approach addresses two key challenges: lacking reasoning step data and limited training samples. To overcome these limitations, OpenRFT leverages domain-specific samples through question augmentation, synthesizing reasoning-process data, and few-shot ICL. The method achieves notable performance gains on SciKnowEval with only 100 domain-specific samples per task. This paradigm offers a new approach to fine-tuning beyond simple pattern imitation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OpenRFT is a way to improve generalist models for specific tasks. It’s like teaching a robot to do a new job by showing it a few examples and helping it understand how to think about the problem. The paper presents a solution that can handle limited data and missing information, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Few shot » Fine tuning
