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New research shows one-step gradient delay is not a barrier for LLM pretraining

By PulseAugur Editorial · [4 sources] ·

A new research paper explores asynchronous pipeline parallelism for large-scale LLM pretraining, challenging the notion that gradient delay is an insurmountable barrier. The study demonstrates that the choice of optimizer significantly impacts performance under a one-step gradient delay, with newer methods like Muon showing greater robustness than traditional optimizers such as AdamW. Researchers also introduced an error feedback-inspired correction to further mitigate delay effects, achieving performance parity with synchronous training on models up to 10 billion parameters. AI

IMPACT This research could enable more efficient and scalable pretraining of large language models by overcoming limitations in current parallelization techniques.

RANK_REASON Research paper published on arXiv detailing a novel approach to LLM pretraining.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New research shows one-step gradient delay is not a barrier for LLM pretraining

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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Philip Zmushko, Egor Petrov, Nursultan Abdullaev, Mikhail Khrushchev, Samuel Horv\'ath ·

    One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Samuel Horváth ·

    One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

    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…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

    Asynchronous pipeline parallelism with PipeDream-2BW can achieve near-synchronous performance through optimizer selection and error feedback correction, overcoming traditional stability concerns.

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