Moonshot Details Kimi Training Path
dotey · x · 2026-07-18
In his GTC 2026 talk How We Scaled Kimi K2.5, Yang Zhilin detailed Moonshot AI's technical roadmap over the past year: how to keep approaching closed-source frontiers using open-source models.
Three core strategies:
- Make every Token more "valuable" by improving high-quality data utilization.
- Make longer contexts genuinely effective.
- Enable multiple Agents to collaborate simultaneously.
He mentioned two specific advances: visual training can inversely enhance text capabilities, and the team just announced their next-gen architecture, Attention Residue. For foundational training components, Moonshot AI has attempted to replace the long-standing Adam optimizer, attention mechanisms, and residual connections, all of which are open-sourced.
Notably, MuonClip replaces Adam. Moonshot claims that for the same amount of data, its training effectiveness nearly doubles the data volume; as high-quality data becomes scarcer, this means higher data efficiency and lower training costs.
Related event: Yang Zhilin Shares Kimi K2.5 Scaling and Efficiency Strategies(4 posts)→
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