“FlashREINFORCE: Critic-Free Single-Rollout Asynchronous RL for Agentic LMs” This paper argues that scalable agent RL doesn’t need multiple rollouts of the same prompt, but can learn from each trajectory as soon as it finishes. They combine reward centering across independent prompts, sequence-level trust regions for stale trajectories, and sample-mean optimization so long failures don’t dominate training, creating stable critic-free learning from just one rollout per prompt. This moves from group-based RL that spends compute repeatedly sampling the same prompts to asynchronous RL where every rollout covers a new prompt, immediately becomes training data, and lets agents learn from more diverse experiences with less rollout compute.