Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective
Agentic reinforcement learning (RL) extends traditional LLM training by optimizing not just a single-turn response, but an entire decision-making process learned through direct interaction with an environment during training. Unlike traditional single-turn reinforcement learning or offline preference-based methods that rely on static datasets, agentic RL trains policies by actively collecting on-policy data as the agent plans actions, invokes tools, observes outcomes, and adapts its behavior over multi-step trajectories in either simulated or real environments. This interaction-driven optimization assigns credit across long-horizon decisions, where intermediate choices such as query reformulation, tool selection, and execution order directly influence downstream success. Training