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rLLM is an open-source framework for building and training post-training language agents via reinforcement learning — that is, using reinforcement signals to fine-tune or adapt language models (LLMs) into customizable agents for real-world tasks. With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to support large-scale language models (including support for big models via integrated training backends), making it relevant for state-of-the-art research and production use. The framework includes tools for defining workflows, specifying objectives or reward functions, and managing training/policy updates across possibly distributed settings.
Features
- Framework for building language agents that learn via reinforcement learning rather than only supervised fine-tuning
- Supports custom agents, environments, reward definitions, and training workflows
- Scales to large models (with integrated training backends) for serious research or production use
- Tools for training, evaluation, and deployment of RL-trained language agents
- Prebuilt agents (e.g. coding agents) demonstrating competitive benchmark performance
- Open-source (Apache 2.0), enabling community contribution, customization, and extension
Project Samples
