rLLM: Reinforcement Learning for Language Agents (rllm-project.readthedocs.io)

rLLM has announced a new framework for training language agents using reinforcement learning, enabling developers to create custom agents and environments while abstracting the complexities of the underlying training infrastructure. This framework makes it easier to define and iterate on training protocols, allowing for continuous enhancement of agent performance through scalable RL training loops. Notably, rLLM unifies the inference and training interfaces, allowing users to seamlessly evolve their agents in real-time based on experiential learning. The latest version, rLLM v0.2.x, introduces significant advancements, including an SDK that transforms agents from popular frameworks like LangGraph into trainable workflows, along with enhancements for multi-agent training and Vision-Language Model (VLM) training. With support for LoRA fine-tuning and integration of the Eval Protocol from Fireworks AI, rLLM offers a powerful toolset for those looking to innovate in AI/ML. The project is open-source and encourages community collaboration, making it a timely resource for advancing AI agent capabilities in various applications.