[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-d6afa8e3-b342-41da-839b-840c02c42cc8":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},"d6afa8e3-b342-41da-839b-840c02c42cc8","ICML 2026 杰出论文砸场子:扩散语言模型的「任意顺序」是个陷阱","ICML 2026 杰出论文奖两篇获奖作品同时花落扩散模型,这种巧合在 ML 三大顶会历史上屈指可数,背后更像是一种集体判断:扩散模型已经进入「纠偏」与「补基建」的深水区。\n\n清华黄高团队与 Zanlin Ni 等人的《The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models》是这届最尖锐的技术反叛。扩散大语言模型 (dLLM) 一直被宣传的核心卖点是「任意顺序生成」——区别于 GPT、Claude 这种从左到右逐 token 蹦的自回归范式,dLLM 像画画一样从噪声里去噪出完整文本,理论上可以先写中间再写开头。但论文用大量实验证明,这个「灵活性」本身就是陷阱:模型为了支持所有可能的生成顺序,反而在每种具体顺序上都做得更差。在通用推理任务上,dLLM 实际上会绕开那些高不确定性的「分叉 token」,导致解空间多样性崩溃。作者提出 JustGRPO:在 RL rollout 阶段回归最朴素的从左到右顺序,推理阶段仍保留并行解码。这个简洁方案戳破了过去两年 dLLM 文献里被反复引用的核心假设——围绕「任意顺序」投入的大量算力和工程优化,理由可能根本站不住。\n\n并列的《High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions》则从理论侧把扩散采样精度画了新天花板:用一阶拒绝采样把 ε-误差所需评分函数调用从 poly(1\u002Fε) 降到 O(d·polylog(1\u002Fε)),把「NFE 还能砍多少倍」从工程优化推到理论上界。\n\n两篇杰出一破一立,加上 DeepMind 2016 年的 A3C 拿下 Test of Time Award——ICML 的信号很明确:扩散语言模型正走出「概念验证」阶段,真正缺的不再是更多花样,而是更冷静的审视、更扎实的理论边界,以及对「灵活即优势」这种直觉的彻底清算。","https:\u002F\u002Fblog.icml.cc\u002F2026\u002F07\u002F05\u002Fannouncing-the-icml-2026-awards\u002F","6569a0a5-7899-4c1c-8ba7-3d03834e87b5",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-07-06T10:00:00Z","2026-07-06T10:16:06.081952Z","2026-07-06T10:16:06.081967Z",true,"agent",3]