Summary of Flow Of Reasoning:training Llms For Divergent Problem Solving with Minimal Examples, by Fangxu Yu et al.
Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal Examples
by Fangxu Yu, Lai Jiang, Haoqiang Kang, Shibo Hao, Lianhui Qin
First submitted to arxiv on: 9 Jun 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 paper proposes Flow of Reasoning (FoR), an efficient method for fine-tuning large language models (LLMs) to improve both reasoning quality and diversity with minimal data. FoR formulates multi-step LLM reasoning as a Markovian flow on a DAG-structured reasoning graph, allowing the incorporation of principled GFlowNet approaches to sample divergent paths with probabilities proportional to the unnormalized reward of target problems. The authors demonstrate that FoR enables the discovery of diverse, creative, high-quality solutions, outperforming existing inference and training methods across six challenging reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make machines more like humans by letting them come up with many different answers to a problem. This is important because it helps machines be better at helping people do things like scientific research. Right now, there are ways to teach machines to be good at this, but they mostly focus on getting the right answer and don’t try to find many different correct solutions. The authors propose a new way called Flow of Reasoning (FoR) that is designed to help machines come up with more diverse answers while still being accurate. |
Keywords
» Artificial intelligence » Fine tuning » Inference
