GRPO has become one of the de facto standard methods for LLM RL training, but when ported to diffusion text-to-image models, the engineering pain points are unusually obvious — Flow-GRPO, DanceGRPO and other solutions need to do SDE sampling and policy optimization at every step along the entire denoising chain, and both training time and memory consumption explode. MixGRPO, proposed by Tencent Hunyuan's team together with Peking University's School of Computer Science / Computer Center, and accepted by ECCV 2026, is a direct response to this pain line. The core approach introduces a "sliding window" mechanism — only do SDE sampling and GRPO optimization within the window, switching to ODE sampling outside the window. This "sandwich" design brings two layers of benefits: first, it compresses the policy update burden to a small subinterval, cutting training time by nearly 50%; second, no reverse gradient is needed outside the window, allowing a higher-order solver to run faster, from which a MixGRPO-Flash variant is derived, cutting training time by another 71%. The authors use HPSv2, ImageReward, and PickScore to form a multi-reward combination on FLUX.1-dev, and MixGRPO exceeds DanceGRPO on human-preference alignment metrics, with correspondingly shortened training cost. Code, checkpoints, and training scripts are all open-sourced (GitHub 1.1k stars), making it a rare industrial-practice-meets-academic-result output. MixGRPO's methodological significance goes beyond saving time — it makes clear the question of "which timesteps actually affect policy updates, which are just forward pass". RL training of diffusion language models will likely evolve along the "localized optimization" path, and MixGRPO is a very typical paradigmatic sample on that path. For engineering teams doing RL-augmented image and video generation, the value of this line of thinking is far beyond saving GPU — it brings the idea of "no need for full-chain gradient" formally into the still-young direction of diffusion RL.