Ant Ling· @AntLingAGI · X·· 2026-05-26精选AI 评分68
AI 导读
团队推出 KPop，用于稳定大规模 MoE 模型的智能体强化学习训练。它用基于二元 KL 散度的自适应掩码机制，替代了此前 IcePop 方法中的固定比例掩码，能根据训练过程中的训练-推理不匹配程度动态调整。这一改进使得 Ring-2.6-1T 模型在无需修改基础设施或路由重放的情况下，仅通过纯 RL 训练，在 SWE-bench Verified 上取得了超过 76 分的成绩。
推荐理由
蚂蚁团队把 IcePop 升级成 KPop，从固定掩码变成自适应 KL 区域，思路很巧。Ring-2.6-1T 纯 RL 直接冲到 SWE-bench 76+，做 agentic RL 训练的同学值得翻一下博客。
正文 · 原文
From IcePop to KPop — our team keeps pushing on RL training stability for large MoE models. 👇
KPop replaces the fixed-ratio mask with an adaptive binary-KL region that matches each token's inherent noise. More robust updates, stable long-horizon agentic RL.
Ring-2.6-1T → 76+ on SWE-bench Verified, pure RL.
Congrats to @Jia__Guo & team!
Curious about the secret sauce behind our trillion-scale agentic foundation model? Here it comes!🥳 Last year, we released IcePop to stabilize MoE RL with double-sided masking. As we dive deeper, something unexpected happened: the masking ratio went down, while the training–inference mismatch continued to grow!😞 This year, we introduce 𝑲𝑷𝒐𝒑🪩, which replaces the fixed ratio constraint with the binary KL divergence to adaptively mask inappropriate tokens! The masking ratio adapts to fluctuations of the training–inference gap during training, keeping policy optimization stable and effective with long-horizon agentic RL rollouts. With this simple change, it enables our Ring-2.6-1T to achieve over 76 on the SWE-bench-Verified with pure RL training! No modifications to infrastructure. No routing replay. Just one parameter, power your agentic RL with 𝑲𝑷𝒐𝒑! Click to learn more about the details! 📜Blog: https://ringtech.notion.site/kpop在 X 查看被引用的帖子
来源：Ant Ling · x.com