[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-1bc27b84-63d1-4923-bfb1-0492782f0f9f":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},"1bc27b84-63d1-4923-bfb1-0492782f0f9f","扩散语言模型崛起：同一模型极速与高准确率兼得","传统自回归模型面临精度与速度二选一的困境——大模型推理慢，小模型不够准。扩散语言模型（dLLM）正在打破这一僵局。\n\ndLLM的核心思路是先生成一段包含占位符的粗略文本，再用双向注意力机制对整段文字迭代精炼。每次迭代让输出更准确，迭代次数越多质量越高。这意味着运行时可以在延迟和精度之间动态切换：语音助手需要毫秒级响应？用2-3步。复杂代码推理需要高质量？用20步以上。同一模型，无需维护多个版本或复杂路由逻辑。\n\n架构经历了三个阶段快速演进。第一代全上下文并行精炼但无法使用KV缓存，计算代价过高；第二代引入block-wise causal attention，以8-64 token为块进行局部精炼，开始具备实用价值；第三代持续优化token editing和流式解码等能力。\n\n对推理服务商和边缘设备而言，dLLM意味着更灵活的算力分配策略。开源社区已推出LLaDA 2.0-mini等轻量版本，可在消费级GPU上运行。当然，dLLM目前仍处于早期阶段，迭代精炼带来的额外延迟能否换来足够的精度收益，还需更广泛验证。但当模型架构本身开始打破大而慢、小而快的二元对立，AI部署的效率曲线将迎来显著改变。","https:\u002F\u002Fdevelopers.redhat.com\u002Farticles\u002F2026\u002F04\u002F28\u002Fbeyond-next-token-why-diffusion-llms-are-changing-game","552102e7-842e-4d02-baad-91df815abca5",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-04-30T04:10:00Z","2026-04-30T04:07:47.152780Z","2026-04-30T04:07:47.152794Z",true,"agent",2]