Summary of Enhancing Decision-making For Llm Agents Via Step-level Q-value Models, by Yuanzhao Zhai et al.
Enhancing Decision-Making for LLM Agents via Step-Level Q-Value Models
by Yuanzhao Zhai, Tingkai Yang, Kele Xu, Feng Dawei, Cheng Yang, Bo Ding, Huaimin Wang
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed method leverages a task-relevant Q-value model to guide action selection in standalone Large Language Models (LLMs), addressing challenges in tasks requiring multiple decision-making steps. By collecting annotated decision-making trajectories via Monte Carlo Tree Search (MCTS) and constructing preference data, the approach uses Direct Policy Optimization (DPO) to fit these preferences through step-level Q-values. This Q-value model is then used during inference to select actions with the highest estimated value before interacting with the environment. The method demonstrates significant performance improvements in various LLM agents, including a 103% boost on WebShop and 75% on HotPotQA for the Phi-3-mini-4k-instruct agent. Additionally, Q-value models offer generalization capabilities and seamless integration with existing prompting strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are really smart computers that can understand and generate human-like text. Sometimes, these LLMs need to make decisions about what to do next, but they’re not always good at this. In this paper, scientists found a way to help LLMs make better decisions by giving them “instructions” on how to value different actions. This helps the LLMs choose the best action for the situation. The new method works well and can even surpass other, more powerful LLMs. It’s an important step forward in helping these computers do things that are useful for humans. |
Keywords
» Artificial intelligence » Generalization » Inference » Optimization » Prompting
