Awesome-Agentic-Reasoning and AgentsMeetRL
These are complementary resources that address different aspects of agent development: the first surveys agentic reasoning approaches applicable across LLM-based systems, while the second specifically focuses on integrating reinforcement learning into agent architectures, making them best used together for a comprehensive understanding of modern agent design.
About Awesome-Agentic-Reasoning
weitianxin/Awesome-Agentic-Reasoning
A curated list of papers and resources based on the survey "Agentic Reasoning for Large Language Models"
This is a curated list of research papers and resources that explore how large language models (LLMs) can be made more autonomous and capable by integrating reasoning with action. It organizes research into foundational abilities like planning and tool use, self-evolving mechanisms through memory and feedback, and collective intelligence in multi-agent systems. Researchers, AI engineers, and students interested in developing or studying advanced LLM-powered agents would find this a valuable resource.
About AgentsMeetRL
thinkwee/AgentsMeetRL
Awesome List for Agentic RL
This project offers a curated list of open-source projects for developing and training AI agents using reinforcement learning. It helps researchers and engineers quickly find relevant code repositories and understand their underlying technical choices, such as RL algorithms, reward mechanisms, and environments. You can easily see what open-source solutions exist for various agent functionalities, from tool use and reasoning to multi-agent systems and safety.
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