一篇新的研究论文探讨了大规模LLM预训练的异步流水线并行方法，挑战了梯度延迟是不可逾越障碍的观点。研究表明，优化器的选择在单步梯度延迟下对性能有显著影响，像Muon这样的较新方法比AdamW等传统优化器更具鲁棒性。研究人员还引入了一种受误差反馈启发的校正方法，以进一步减轻延迟效应，在参数量高达100亿的模型上实现了与同步训练相当的性能。 AI
影响 这项研究通过克服当前并行化技术的局限性，有望实现更高效、可扩展的大型语言模型预训练。
排序理由 在arXiv上发表的研究论文，详细介绍了一种新颖的LLM预训练方法。
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arXiv:2606.30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism elimin…
Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism eliminates these bubbles, maximizing throughput at the…
Asynchronous pipeline parallelism with PipeDream-2BW can achieve near-synchronous performance through optimizer selection and error feedback correction, overcoming traditional stability concerns.