Show HN: ART – a new open-source RL framework for training agents

Summary

Train GRPO-powered RL agents with minimal code changes and maximal performance! Agent Reinforcement Trainer (ART) ART is an open-source reinforcement training library for improving LLM performance in agentic workflows. ART utilizes the powerful GRPO reinforcement learning algorithm to train models from their own experiences. Unlike most RL libraries, ART allows you to execute agent runs in your existing codebase while offloading all the complexity of the RL training loop to the ART backend. Read about the training loop. Then try out one of the notebooks below! 📒 Notebooks 🔁 Training Loop Overview ART's functionality is divided into a client and a server. The OpenAI-compatible client is responsible for interfacing between ART and your codebase. Using the client, you can pass messages and get completions from your LLM as it improves. The server runs independently on any machine with a GPU. It abstracts away the complexity of the inference and training portions of the RL loop while allowing for some custom configuration. An outline of the training loop is shown below: Inference Your code uses the ART client to perform an agentic workflow (usually executing several rollouts in parallel to gather data faster). Completion requests are routed to the ART server, which runs the model's latest LoRA in vLLM. As the agent executes, each system , user , and assistant message is stored in a Trajectory. When a rollout finishes, your code assigns a reward to its Trajectory, indicating the performance of the LLM. Training When each rollout has finished, Trajectories are grouped and sent to the server. Inference is blocked while training executes. The server trains your model using GRPO, initializing from the latest checkpoint (or an empty LoRA on the first iteration). The server saves the newly trained LoRA to a local directory and loads it into vLLM. Inference is unblocked and the loop resumes at step 1. This training loop runs until a specified number of inference and training ite...