Summary of Watch Every Step! Llm Agent Learning Via Iterative Step-level Process Refinement, by Weimin Xiong et al.
Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement
by Weimin Xiong, Yifan Song, Xiutian Zhao, Wenhao Wu, Xun Wang, Ke Wang, Cheng Li, Wei Peng, Sujian Li
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Large language model agents have shown impressive performance across various complex interactive tasks. Recent approaches have used expert trajectories for tuning, but these primarily focus on outcome rewards, which can lead to errors or suboptimal actions due to the absence of process supervision signals. To address this, we introduce the Iterative step-level Process Refinement (IPR) framework, which provides detailed guidance for agent training using Monte Carlo method-estimated step-level rewards. Our approach evaluates new actions against expert trajectories and identifies discrepancies, generating contrastive action pairs for training. We demonstrate the effectiveness of IPR on three complex tasks, outperforming strong baselines and highlighting its applicability to diverse models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart computer program that can learn from experts in various fields. Researchers have been trying to improve these “agents” by giving them feedback on what they’re doing right or wrong. But sometimes, this feedback only focuses on the final result, not how the agent got there. This paper introduces a new way to help agents learn by providing detailed step-by-step guidance. It’s like having a mentor who shows you exactly what to do and why. The results show that this approach works better than others, even with different types of agents. |
Keywords
* Artificial intelligence * Large language model
