Summary of React Meets Actre: When Language Agents Enjoy Training Data Autonomy, by Zonghan Yang et al.
ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
by Zonghan Yang, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 proposed framework, A^3T, enables autonomous annotation of language agent trajectories by leveraging ActRe prompting agents that provide textual rationales for actions. This allows for the synthesis of novel trajectories through prepending posterior reasoning from ActRe to sampled actions. The ReAct-style agent executes multiple trajectories for failed tasks and selects successful ones for contrastive self-training using policy gradient methods with binarized rewards. A^3T facilitates a closed loop for language agent self-improvement, demonstrating promising results in AlfWorld and WebShop. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A^3T is a new way to train language agents that can make decisions on their own. Right now, making these agents better requires a lot of human effort. The authors propose a system that allows the agent to explain its actions and use this explanation to learn from its mistakes. This helps the agent improve its decision-making skills over time. In two scenarios, AlfWorld and WebShop, the A^3T agent performed very well, almost as well as humans. |
Keywords
* Artificial intelligence * Prompting * Self training
