Summary of From Novice to Expert: Llm Agent Policy Optimization Via Step-wise Reinforcement Learning, by Zhirui Deng et al.
From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning
by Zhirui Deng, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen, Ruibin Xiong, Mang Wang, Weipeng Chen
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces StepAgent, a reinforcement learning strategy to optimize autonomous agents’ ability to solve complex interactive tasks with environments and tools. Unlike traditional approaches that solely rely on the inherent knowledge of large language models (LLMs), StepAgent utilizes step-wise rewards to fine-tune the agent’s policy learning process. The approach draws inspiration from novice-to-expert theory, automatically generating intermediate rewards for fine-grained optimization by comparing expert actions with those of the agent. Implicit-reward and inverse reinforcement learning techniques are also proposed to facilitate agent reflection and policy adjustment. Experimental results across various datasets demonstrate that StepAgent outperforms existing baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to train computers to make decisions in complex situations, like playing games or doing tasks with tools. Right now, these computers rely too much on their built-in knowledge, but the new approach lets them learn from trial and error. The problem is that most training data only gives a final score for each attempt, which isn’t very helpful. This paper introduces a new system called StepAgent that gives the computer smaller rewards along the way to help it improve its decision-making. The authors also suggest two other ways to make the computer learn from its mistakes and adjust its strategy. They tested their approach on several different datasets and found that it works better than current methods. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
