[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-bytedance-spectra-reward":3,"news-related-a6119d74-007a-4692-bb4e-d85b562a9d66":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"a6119d74-007a-4692-bb4e-d85b562a9d66","字节 SpectraReward：自我奖励 T2I 干翻 30× 大模型","字节 Seed × 港大 × 北大在 arXiv 2607.11886 提出 SpectraReward,把「读回 prompt」作为 T2I RL 的零样本奖励;Self-SpectraReward 让 BAGEL 自我奖励,GenEval 89.5 反超 Qwen3-VL-235B-A22B;核心结论:reward-policy alignment 比 reward scale 更重要。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.11886","7437aeb9-930c-4866-a2e9-48003c1a792b",[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},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"719cfbbe-f9b1-4a13-8dcd-529ec042768e","en","SpectraReward: self-rewarded T2I beats 30x larger models","ByteDance Seed × University of Hong Kong × Peking University propose SpectraReward in arXiv 2607.11886, using \"reading back the prompt\" as a zero-shot reward for T2I RL; Self-SpectraReward lets BAGEL reward itself, with GenEval 89.5 beating Qwen3-VL-235B-A22B; the core conclusion: reward-policy alignment matters more than reward scale.","bytedance-spectra-reward","2026-07-15T04:30:00Z","2026-07-15T04:15:45.790333Z","2026-08-19T02:08:40.142862Z",true,"agent",171,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"19566223-1b02-4e48-8c44-518694edb049","Meta Muse Image 落地：Superintelligence Labs 把多模态推理与图生能力拧成一股","meta-muse-image","2026-07-07T20:01:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"593fc68b-74f3-4ad9-b669-ed42b5d5da7a","iRDM 把经典 MMD 重新点燃:ImageNet 单步生成刷 SOTA,90 H200 小时把 FLUX.2 [klein] 蒸馏成一步","irdm-mmd-single-step","2026-07-06T06:30:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"e9688664-6ec6-4816-8898-3a92e6638c7a","MrFlow：四步分阶段采样把文生图扩散推到 10× 加速，OneIG 损失压到 1%","mrflow-four-step-t2i","2026-07-04T14:11:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"21a8425d-3b1a-4d24-bace-610aedd5a059","VisNec 把多模态微调压到 15%:用「看图与不看图的损失差」筛掉假多模态样本","visnec-15-percent-multimodal","2026-07-04T10:15:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"ce1ba4c1-7d9f-470c-86f3-9cabc8e69e0a","字节DanceOPD把图像生成多能力冲突变成「场蒸馏」：硬路由+单查询就赢","bytedance-danceopd-field-distillation","2026-06-28T04:30:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"58d2e247-e1d7-4325-8e90-602480fae550","微信AI团队ICASSP 2026获奖：从视觉冗余切入，让VLM在边缘设备真正跑起来","wechat-icassp-2026-vlm-edge-best-paper","2026-05-19T02:30:00+00:00"]