Unverified50% confidenceFactExact time
The MuonW and MuonH optimizers can guarantee at least linear growth of the cumulative effective learning rate under given conditions.
1
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50%
Confidence
Long-term
Relevance
10/28/2026
First Seen
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Related Claims
UnverifiedLISCO (Learned Iterative Solver for Constrained Optimization) learns to predict primal-dual step sizes to iteratively approach a solution satisfying KKT optimality conditions, rather than directly predicting the final control output.68% similarUnverifiedThe MUON optimizer was first validated in the Kimi model.66% similarUnverifiedHimanshu指出LLM推理管线的最大优势在于可微分，可用强化学习优化，但无法学习代数构造63% similarUnverifiedThe cumulative effective learning rate C_T = Σ_{t<T} η_t / ||W_t||_F links weight-norm control with the optimizer's exploration capability.62% similarUnverifiedAnother possibility is to rely on models with continual learning capability, enabling them to meaningfully undergo an ontological development process.62% similar
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