FlashREINFORCE: Critic-Free Single-Rollout Asynchronous RL for Agentic Language Models

Asynchronous reinforcement learning suits long-horizon agents with irregular rollout times, but group-relative updates require repeated rollouts of each prompt. However, group-based sampling limits prompt coverage at a fixed rollout budget and introduces group synchronization barriers. One rollout per prompt avoids these constraints but removes the variance-reducing group baseline, making updates noisier and more prone to instability on stale, variable-length trajectories. We present FlashREINFORCE, a critic-free, one-pass framework built on One-Batch REINFORCE, a Sequence Trust Region, and Sample-Mean Optimization. These components preserve prompt coverage and signed feedback while controlling policy drift and length-amplified negative updates. In our experiments, FlashREINFORCE remains stable through 6,000 updates on DeepSeek-R1-Distill-Qwen-1.5B at a policy lag of approximately four updates. On Qwen2.5-Math-1.5B, it reaches 38.0 mean accuracy across five benchmarks, outperforming the reported GRPO baseline by 1.7 percentage points with half the rollouts (256k versus 512k). Python-tool experiments extend to the 30B MoE model Qwen3-30B-A3B, with stable training at policy lag eight. On Qwen2.5-7B-Instruct, FlashREINFORCE maintains tool use where GRPO stops making tool calls and achieves 98.3%/96.5% seen/unseen ALFWorld success.