Summary of Cops: Empowering Llm Agents with Provable Cross-task Experience Sharing, by Chen Yang et al.
CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing
by Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou
First submitted to arxiv on: 22 Oct 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 The proposed CoPS algorithm enhances sequential reasoning in agent systems by cross-task experience sharing and selection, addressing limitations of existing approaches. By leveraging agents’ experiences on previous tasks, selecting distribution-matched experiences via a provable pessimism-based strategy, CoPS maximizes utility while minimizing risks from distribution shifts. Experimental results on benchmarks like Alfworld, Webshop, and HotPotQA demonstrate that CoPS consistently outperforms state-of-the-art baselines, with superior sample efficiency suitable for resource-constrained scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoPS is a new way to help machines learn from their experiences across different tasks. Instead of just relying on knowledge from big language models, it shares information between tasks and selects the most useful experiences. This makes the machine more efficient in learning and more adaptable to new situations. In tests on various benchmarks, CoPS performed better than existing methods and was able to learn faster with limited data. |
