Summary of Agent Q: Advanced Reasoning and Learning For Autonomous Ai Agents, by Pranav Putta and Edmund Mills and Naman Garg and Sumeet Motwani and Chelsea Finn and Divyansh Garg and Rafael Rafailov
Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
by Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, Rafael Rafailov
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The proposed framework combines guided Monte Carlo Tree Search (MCTS) with a self-critique mechanism and iterative fine-tuning on agent interactions using an off-policy variant of the Direct Preference Optimization (DPO) algorithm. This allows Large Language Model (LLM) agents to learn effectively from both successful and unsuccessful trajectories, improving their generalization in complex, multi-step reasoning tasks. The framework outperforms behavior cloning and reinforced fine-tuning baselines in the WebShop environment and beats average human performance when equipped with online search capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have amazing abilities in understanding language, but they struggle to make decisions on their own. This paper proposes a new way for these models to learn from mistakes and successes, making them better at decision-making. The approach combines two techniques: Monte Carlo Tree Search (MCTS) and self-critique. This allows the models to improve over time, getting better at solving complex problems. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Large language model » Optimization
