Summary of Agile: a Novel Reinforcement Learning Framework Of Llm Agents, by Peiyuan Feng et al.
AGILE: A Novel Reinforcement Learning Framework of LLM Agents
by Peiyuan Feng, Yichen He, Guanhua Huang, Yuan Lin, Hanchong Zhang, Yuchen Zhang, Hang Li
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper presents AGILE, a novel reinforcement learning framework that leverages large language models (LLMs) to perform complex conversational tasks. The AGILE agent is designed to interact and learn from environments, utilizing LLMs, memory, tools, and expert consultations. By formulating the construction of this agent as a reinforcement learning problem, the authors fine-tune an LLM using labeled data and the PPO algorithm. The authors focus on question answering and release a challenging dataset called ProductQA for agents. Experimental results show that AGILE agents based on 7B and 13B LLMs trained with PPO outperform GPT-4 agents on various datasets, including ProductQA, MedMCQA, and HotPotQA. An ablation study highlights the importance of memory, tools, consultation, reflection, and reinforcement learning in achieving strong performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AGILE is a new way for computers to have conversations with humans. It uses special language models that get better at talking when they learn from experts and practice. The computer can even ask questions and use tools to help it talk better. Scientists tested AGILE on different tasks, like answering tricky questions about online shopping, and found that it could do a great job. This means we might see computers having more natural conversations with humans in the future. |
Keywords
» Artificial intelligence » Gpt » Question answering » Reinforcement learning
