Summary of Tree Search For Language Model Agents, by Jing Yu Koh et al.
Tree Search for Language Model Agents
by Jing Yu Koh, Stephen McAleer, Daniel Fried, Ruslan Salakhutdinov
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper proposes a new approach to decision-making tasks for language model (LM) powered autonomous agents. The authors highlight the limitations of existing LMs in performing multi-step reasoning, planning, and using environmental feedback when solving computer tasks. To address this, they introduce an inference-time search algorithm that allows LM agents to explicitly explore and plan in interactive web environments. This approach is a form of best-first tree search that operates within the actual environment space, complementing existing state-of-the-art agents. The authors demonstrate the effectiveness of their approach on realistic web tasks, achieving a 39.7% relative increase in success rate on the VisualWebArena benchmark and a 28.0% relative improvement on WebArena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps autonomous agents powered by language models make better decisions. Right now, these agents struggle to think ahead, use feedback from the environment, and solve problems that require multiple steps. The authors come up with a new way for these agents to explore and plan in interactive web environments. They test this approach on real-world tasks and show that it works well, improving success rates by a lot. This is an important step forward in making autonomous agents more useful. |
Keywords
* Artificial intelligence * Inference * Language model
