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New Token Superposition method slashes LLM pre-training time by 2.5x

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed a new pre-training method called Token-Superposition Training (TST) that aims to make large language model training more efficient. TST involves a two-phase process: an initial superposition phase where tokens are combined and trained with a multi-hot cross-entropy objective, followed by a recovery phase of standard training. Evaluations on models up to 10 billion parameters show TST can reduce pre-training time by up to 2.5x under equal-loss conditions. AI

影响 This method could significantly reduce the computational cost and time required for training large language models, potentially accelerating research and development.

排序理由 The cluster contains an academic paper detailing a new method for pre-training large language models.

在 arXiv cs.CL 阅读 →

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New Token Superposition method slashes LLM pre-training time by 2.5x

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Bowen Peng, Th\'eo Gigant, Jeffrey Quesnelle ·

    Efficient Pre-Training with Token Superposition

    arXiv:2605.06546v1 Announce Type: new Abstract: Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data throughput. In this work, we present Token-Superposition Tra…

  2. arXiv cs.CL TIER_1 English(EN) · Jeffrey Quesnelle ·

    Efficient Pre-Training with Token Superposition

    Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data throughput. In this work, we present Token-Superposition Training (TST), a simple drop-in method that signif…

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