Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective

Agentic reinforcement learning (RL) enhances traditional large language model (LLM) training by optimizing multi-step decision-making through direct environment interaction, unlike single-turn or offline methods relying on static datasets. This approach involves collecting on-policy data as agents plan, invoke tools, and adapt their behavior, thereby influencing downstream success over long trajectories. The article discusses the development of agentic RL for the GPT-OSS model, with experiments using the verl framework to address challenges in RL training, such as log-probability mismatches in Mixture of Experts (MoE) architectures and ensuring on-policy integrity in Proximal Policy Optimization (PPO). Key solutions include fixing training-inference mismatches by implementing attention sinks in FlashAttention v3, which improved training stability and convergence. Memory-efficient strategies and sequence parallelism were also employed to manage extensive context windows necessary for multi-step agentic training. These efforts validated GPT-OSS as a scalable model for intelligent multi-step decision-making agents, with contributions towards stabilizing PPO, enhancing attention sink support, and optimizing memory usage.