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Anytime Pretraining offers horizon-free LLM training with weight averaging

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced "Anytime Pretraining," a novel approach to training large language models that eliminates the need for pre-defined training horizons. This method utilizes horizon-free learning-rate schedules combined with weight averaging, demonstrating comparable final loss to traditional cosine decay schedules. The findings suggest that this anytime strategy offers a practical and effective alternative for large language model pretraining, particularly in open-ended training scenarios. AI

IMPACT This research could simplify LLM training by removing the need for complex, horizon-dependent learning rate schedules.

RANK_REASON The cluster contains a research paper detailing a new method for training large language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Anytime Pretraining offers horizon-free LLM training with weight averaging

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The cluster contains a research paper detailing a new method for training large language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alexandru Meterez, Pranav Ajit Nair, Depen Morwani, Cengiz Pehlevan, Sham Kakade ·

    Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging

    arXiv:2602.03702v2 Announce Type: replace-cross Abstract: Large language models are increasingly trained in continual or open-ended settings, where the total training horizon is not known in advance. Despite this, most existing pretraining recipes are not anytime: they rely on ho…

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