Report Abuse

Basic Information

Trinity-RFT is a general-purpose framework for reinforcement fine-tuning of large language models. It provides a unified platform for researchers and developers to design, run, and scale reinforcement learning workflows that adapt LLM behavior to new tasks and interactive scenarios. The repository supports building agent-environment interaction logic in single-step, multi-turn, and general multi-step workflows and includes examples and tutorials for common benchmarks and agentic tasks. It targets experimental and production-style RFT use cases by offering modular components for rollout, training, data processing, and configuration. The project includes a web-based studio for low-code configuration, command-line tools for running experiments, and support for common model and dataset sources to accelerate reproduction and extension of RFT experiments.

Links

Categorization

App Details

Features
Trinity-RFT provides a unified RFT core that supports synchronous and asynchronous modes, on-policy and off-policy algorithms, and online and offline training. Rollout and training are decoupled so they can scale independently across devices. The framework includes first-class agent-environment interaction handling to tolerate lagged feedback, long-tailed latencies, and agent or environment failures and supports multi-turn interactions. Data pipelines are optimized for dynamic experience management including prioritization, cleaning, and augmentation. The architecture is modular and extensible, enabling plug-and-play RL algorithm development. Usability features include a web-based studio, example configuration files, Docker support, tutorials for common modes, Ray-based distributed execution, and optional integration with experiment tracking.
Use Cases
This repository helps practitioners and researchers perform reinforcement fine-tuning of LLMs in realistic interactive settings. It lowers engineering overhead by providing reusable workflow classes for agent-environment logic, templates and tutorials for common RFT modes, and modular hooks for custom loss and sampling designs. The decoupled rollout and training design enables flexible distributed execution on multi-GPU clusters. Robust data pipelines and experience management improve training signal quality and reproducibility. The web studio and example configs speed onboarding for users who prefer low-code experiment setup while command-line and Docker options support production experiments. Overall, Trinity-RFT enables systematic exploration and deployment of advanced RL paradigms for adapting large language models.

Please fill the required fields*