Flow-Map GRPO: opens the door to reinforcement learning for deterministic "few-step image generation" Few-step Flow-Map generators — like consistency models, sCM, MeanFlow — have been the fastest tier in image diffusion models over the past two years: they directly learn the "long-distance transport map" from noise to data, compressing the sampling steps to single digits. But determinism is their Achilles' heel: online RL post-training methods like GRPO and PPO that need random trajectories and good likelihood ratios have long been unable to be directly applied. Flow-Map GRPO (arXiv:2607.00535, July 1) solves this bottleneck. Its core mechanism is ASFMC (Anchored Stochastic Flow Map Composition): injects randomness through anchored conditional resampling while fully preserving the original Flow-Map's marginal probability path. This doesn't break the efficiency of few-step generation, while making the GRPO objective function differentiable. The authors also derive GRPO objectives applicable to both one-step and two-step Flow-Map parameterizations, and verify on MeanFlow and sCM based on the FLUX backend, with multiple reward / perception / task-level metrics all rising. The most noteworthy is its engineering philosophy: no retraining needed. Flow-Map GRPO makes post-training a "plug-in" module, directly aligning pretrained deterministic generators, without changing parameterization, and without having to retrain the model into a native stochastic model — extremely friendly to existing checkpoints, which also means FLUX, Qwen-Image, Wan and other already-online few-step workflows can in the future all hook up RL at low cost. For the GRPO+diffusion community, this is another signal after the late-June Qwen-Image-2.0-RL tech report: the post-training paradigm is radiating from LLM to image generation, and few-step Flow-Map has been the missing link on this extension path. After Flow-Map GRPO fills it, the remaining things for the industry are mainly engineering — more stable reward models, larger-scale human-preference alignment, controllable RL for specific styles, will all unfold along this opening.