Summary of Malt: Improving Reasoning with Multi-agent Llm Training, by Sumeet Ramesh Motwani et al.
MALT: Improving Reasoning with Multi-Agent LLM Training
by Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das, Rafael Rafailov, Ivan Laptev, Philip H. S. Torr, Fabio Pizzati, Ronald Clark, Christian Schroeder de Witt
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces MALT (Multi-Agent LLM Training), a novel post-training strategy that enables Large Language Models (LLMs) to explore reasoning paths and self-correct flawed outputs in complex tasks. MALT divides the reasoning process into generation, verification, and refinement steps using a sequential pipeline of heterogeneous agents. The approach employs value iteration to propagate reward signals back to each role-conditioned model, producing multi-agent post-training data without human or teacher-model supervision. This off-policy method allows each agent to specialize by learning from correct and incorrect trajectories, ultimately improving the end-to-end reasoning chain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MALT helps Large Language Models (LLMs) get better at thinking through problems. Right now, LLMs just give a single answer without considering different ways to solve the problem. MALT changes this by breaking down the process into three steps: coming up with an idea, checking if it’s correct, and making any necessary improvements. This helps the model learn from both its successes and failures. |
Keywords
» Artificial intelligence » Teacher model
