Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models

Xuchen Pan; Yanxi Chen; Yushuo Chen; Yuchang Sun; Daoyuan Chen; Wenhao Zhang; Yuexiang Xie; Yilun Huang; Yilei Zhang; Dawei Gao; Weijie Shi; Yaliang Li; Bolin Ding; Jingren Zhou

arXiv · 2505.17826

Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models

Xuchen Pan·Yanxi Chen·Yushuo Chen·Yuchang Sun·Daoyuan Chen·Wenhao Zhang·Yuexiang Xie·Yilun Huang·Yilei Zhang·Dawei Gao·Weijie Shi·Yaliang Li·Bolin Ding·Jingren Zhou

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Abstract

Trinity-RFT is a general-purpose, unified and easy-to-use framework designed for reinforcement fine-tuning (RFT) of large language models. It is built with a modular and decoupled design, consisting of (1) an RFT-core that unifies and generalizes synchronous/asynchronous, on-policy/off-policy, and online/offline modes of RFT; (2) seamless integration for agent-environment interaction with high efficiency and robustness; and (3) systematic data pipelines optimized for RFT. Trinity-RFT can be easily adapted for diverse application scenarios, and serves as a unified platform for development and research of advanced reinforcement learning paradigms at both macroscopic and microscopic levels. This technical report outlines the vision, features, design and implementations of Trinity-RFT, accompanied by extensive examples, applications and experiments that demonstrate its functionalities and user-friendliness.

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Xuchen Pan, Yanxi Chen, Yushuo Chen, Yuchang Sun, Daoyuan Chen, Wenhao Zhang, Yuexiang Xie, Yilun Huang, Yilei Zhang, Dawei Gao, Weijie Shi, Yaliang Li, Bolin Ding, Jingren Zhou. 2025-09-29. Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models. https://arxiv.org/abs/2505.17826

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