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This paper challenges the assumption that one-step gradient delay in asynchronous pipeline parallelism is inherently unstable, showing that degradation depends on optimizer choice. It demonstrates that optimizers like Muon are robust to one-step delay and introduces an error-feedback correction to further mitigate staleness, achieving near-synchronous performance in LLM pretraining up to 10B parameters.