AMAP-ML/Tree-GRPO
[ICLR 2026] Tree Search for LLM Agent Reinforcement Learning
This project helps AI researchers and developers improve how large language models (LLMs) answer complex questions. By using a 'tree-search' approach instead of simpler methods, it makes LLM agents more accurate and efficient. You input an LLM and question-answering datasets, and it outputs a more capable, optimized LLM agent for various QA tasks.
304 stars.
Use this if you are developing or fine-tuning LLM agents for complex question-answering and want to achieve better performance with fewer computational resources.
Not ideal if you are looking for a plug-and-play solution for basic LLM applications or do not have experience with reinforcement learning and LLM agent training.
Stars
304
Forks
25
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 26, 2026
Commits (30d)
0
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