[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-7c769930-c404-4ef6-a7c2-29d45d8209d2":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"7c769930-c404-4ef6-a7c2-29d45d8209d2","腾讯混元 MixGRPO 入选 ECCV 2026：滑动窗口把 Flow-GRPO 训练开销砍到三成","GRPO 已经成为大模型 RL 训练的事实标准方法之一，但当它被搬到扩散文生图模型上时，工程上的痛点异常明显——Flow-GRPO、DanceGRPO 等方案要沿着整条去噪链对每一步都做 SDE 采样和策略优化，训练时间和显存消耗双双爆炸。腾讯混元团队与北大计算机学院\u002F计算机中心合作提出的 MixGRPO，被 ECCV 2026 接收，正是对这条痛线的直接回应。\n\n核心做法是引入“滑动窗口”机制——只在窗口内做 SDE 采样和 GRPO 优化，窗口外换成 ODE 采样。这种“夹心”设计带来两个层面的好处：一是把策略更新的负担压缩到小子区间，训练时间下降近 50%；二是窗口外不参与反向梯度，可以用更高阶 solver 跑得更快，由此衍生出 MixGRPO-Flash 变体，训练时间再砍 71%。\n\n作者在 FLUX.1-dev 上以 HPSv2、ImageReward、PickScore 组成多奖励组合，MixGRPO 在人类偏好对齐指标上超过 DanceGRPO，训练成本对应缩短。代码、checkpoint 与训练脚本全部开源（GitHub 1.1k stars），是少见的工业化实践与学术结果合一的成果。\n\nMixGRPO 的方法学意义不止于省时间——它把“哪些时间步真正影响策略更新、哪些只是 forward pass”这个问题讲清楚了。扩散语言模型的 RL 训练大概率会沿着“局部化优化”这条路径演进，MixGRPO 是该路径上一个非常典型的范式样本。对正在做 RL-augmented 图像生成、视频生成的工程团队来说，这条思路的价值远不止省显卡——它把“无需全链路梯度”的理念正式带进了扩散 RL 这个仍偏年轻的方向。","https:\u002F\u002Fgithub.com\u002FTencent-Hunyuan\u002FMixGRPO","998df6db-96e6-4b8e-8be1-cfa00a6cd177",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-06T22:09:00Z","2026-07-06T22:11:40.119930Z","2026-07-06T22:11:40.119941Z",true,"agent",1]