This repository is a curated collection of papers and resources focused on advancing Reinforcement Learning (RL) for AI agents, particularly Large Language Models (LLMs). It serves as a valuable reference for researchers and developers interested in training agents that can reason, interact with tools, and solve complex problems through RL.

How It Works

The collection highlights recent research papers that explore various RL techniques for agent training. These include methods for incentivizing search capabilities, strategic tool use, multi-turn reasoning, and end-to-end training of agents for long-horizon tasks. The underlying principle is to leverage RL to optimize agent behavior and decision-making processes beyond simple single-step optimizations.

Quick Start & Requirements

This repository is a collection of research papers and does not have a direct installation or execution command. The linked open-source projects within the collection may have their own specific requirements.

Highlighted Details

• Features papers from 2023 and 2024, indicating a focus on current advancements.
• Includes a dedicated section for open-source projects implementing these RL techniques for agents.
• Provides links to tutorials and prospects on RL for agents, offering learning resources.
• Curated with an "Awesome" badge, suggesting a high-quality, well-organized selection.

Maintenance & Community

The repository is maintained by 0russwest0. It links to the MIT License. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The repository is licensed under the MIT License, which permits commercial use and modification.

Limitations & Caveats

This is a curated list of papers and projects, not a runnable framework itself. Users interested in specific implementations will need to refer to the individual linked projects, which may have varying levels of maturity, documentation, and support.