[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-070aef27-5fdb-4f0a-8b90-99afc1ea34fb":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},"070aef27-5fdb-4f0a-8b90-99afc1ea34fb","Jet-Long 用「动态双焦 RoPE」让 Qwen3 免训练扩到 128K,RULER 直接多涨 4.79 pp","长上下文外推一直是开源 LLM 的痛点:现有方法要么靠昂贵的继续训练,要么用单个 rescaling 因子粗暴外推——激进则在短上下文崩盘,保守则在长上下文力不从心。MIT 韩松团队 7 月 8 日放出的 Jet-Long (arXiv:2607.07740) 给出了一个相当简洁的解法:核心是把 RoPE 的位置编码想象成「双焦镜头」——一组窗口保持原始 RoPE 不动(保住短上下文保真度),另一组窗口的 rescaling 因子根据当前序列长度动态调整,负责长程外推。两组窗口通过 inclusion–exclusion 的方式合并注意力,并在推理时即时旋转校正 RoPE。整个机制纯算法层,无需任何微调。工程上作者把它熔进单个 CuTe kernel,在 H100 上 long-context prefill 达到 FA2 的 1.39× 吞吐(逼近 Hopper-only 的 FA4),单 batch 生成开销 ≤4%——之前零样本外推方法常把吞吐砍半。效果上,Qwen3-1.7B\u002F4B\u002F8B 在 128K 上下文上 RULER 比最强基线高 +4.79\u002F+2.18\u002F+2.03 pp,HELMET-RAG 综合第一,PG-19 困惑度最低。论文还演示了把 Jet-Long 直接套到 Jet-Nemotron 这种混合注意力架构上继续涨点——说明「双焦」思路与底层架构正交。对开源社区的实际意义:任何训好的 Qwen3 checkpoint 都能在 10 分钟内获得 128K 上下文能力,不需要继续训练、不需要合成数据、不需要换架构。这把 long-context tax 的门槛降到前所未有的低。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.07740","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-07-12T02:30:00Z","2026-07-12T02:08:27.562744Z","2026-07-12T02:08:27.562752Z",true,"agent",3]