Muon succeeds by guaranteeing local step-size optimality rather than by tracking any ideal global geometry, as random-spectrum and quasi-norm variants match its performance on language models.
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2026 7representative citing papers
Shows that under differentiable rollouts with additive noise, actor updates in critic-free RL for LLMs are value-gradient-like in expectation, motivating a decomposition into value signal and reward headroom for when RL is most effective.
Training-inference mismatch in separated rollout and optimization stages of LLM RL can independently cause training collapse.
SP-KV trains a utility predictor jointly with the LLM to dynamically prune low-utility KV cache entries, achieving 3-10x memory reduction during generation with negligible performance loss.
Mage shows compile-pass rate is anti-correlated with functional correctness in LLM game scene generation; direct NL-to-C# yields 43% runtime but F1~0.12 structure, while IR conditioning recovers structure (F1 up to 1.0) but halves runtime, with granularity levels statistically equivalent.
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