Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement
  Fine-Tuning of Large Language Models
Trinity-RFT is a general-purpose, flexible and scalable framework designed
for reinforcement fine-tuning (RFT) of large language models. It is built with
a 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 exploring advanced reinforcement learning paradigms.
This technical report outlines the vision, features, design and implementations
of Trinity-RFT, accompanied by extensive examples demonstrating the utility and
user-friendliness of the proposed framework.
6833ce1bd5c438959f750d57
Xuchen Pan
6833ce1bd5c438959f750d58
Yanxi Chen
claimed_verified
2025-05-29T07:44:36.860Z
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/avatars/f801f551640caa70368fcc26a0f51d27.svg
Yanxi Chen
user
yanxi-chen
6833ce1bd5c438959f750d59
Yushuo Chen
6833ce1bd5c438959f750d5a
Yuchang Sun
6833ce1bd5c438959f750d5b
Daoyuan Chen
6833ce1bd5c438959f750d5c
Wenhao Zhang
6833ce1bd5c438959f750d5d
Yuexiang Xie
6833ce1bd5c438959f750d5e
Yilun Huang
claimed_verified
2025-07-07T11:26:04.394Z
64213e9d9628d83384af2632
/avatars/46a34fe25b139acf59f8e5a665999ba1.svg
Dylan Huang
user
hylcool
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Yilei Zhang
6833ce1bd5c438959f750d60
Dawei Gao
6833ce1bd5c438959f750d61
Yaliang Li
6833ce1bd5c438959f750d62
Bolin Ding
6833ce1bd5c438959f750d63
Jingren Zhou
Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement
  Fine-Tuning of Large Language Models
Trinity-RFT is a general-purpose, flexible and scalable framework designed
for reinforcement fine-tuning (RFT) of large language models. It is built with
a 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 exploring advanced reinforcement learning paradigms.
This technical report outlines the vision, features, design and implementations
of Trinity-RFT, accompanied by extensive examples demonstrating the utility and
user-friendliness of the proposed framework.
Trinity-RFT is a flexible and scalable framework for reinforcement fine-tuning of large language models, supporting various interaction modes and data pipelines.
reinforcement fine-tuning RFT-core synchronous/asynchronous on-policy/off-policy online/offline agent-environment interaction data pipelines reinforcement learning paradigms