According to Tilde Research, the Muon optimizer adopted by leading AI models including DeepSeek V4 and Kimi K2.5 has a hidden flaw: it causes over 25% of MLP layer neurons to permanently die during early training. The team designed Aurora, an alternative optimizer, and open-sourced it. A 1.1B parameter model trained with only 100B tokens matched the performance of Qwen3-1.7B trained on 36T tokens across language understanding benchmarks like HellaSwag and Winogrande, demonstrating roughly 100x data efficiency improvement. Aurora adds 6% computational overhead compared to Muon and can serve as a direct replacement.
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