Summary of Online Joint Fine-tuning Of Multi-agent Flows, by Paul Mineiro
Online Joint Fine-tuning of Multi-Agent Flows
by Paul Mineiro
First submitted to arxiv on: 6 Jun 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 paper proposes a novel procedure for constructing and fine-tuning entire flows of component models, known as Agents, which iteratively communicate to solve complex problems. This approach builds upon the Learning to Search framework and leverages simulator access to reduce preferences over entire episodes to preferences over individual node outputs. The method is applicable to both reward-based and reward-free settings, including text feedback scenarios. The authors apply this procedure to the multi-hop QA dataset Musique, achieving a state-of-the-art result. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores new ways to build complex problem-solving systems called flows. Flows are made up of smaller models that work together to find solutions. Right now, building these flows requires combining manual tweaks with computer learning techniques. The authors develop a new approach that lets them fine-tune entire flows by focusing on individual steps rather than the whole process. This method can be used in situations where there’s no reward or feedback, like when people give text feedback. |
Keywords
» Artificial intelligence » Fine tuning
