Multi-Token Prediction Done Right
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper identifies a flaw in standard multi-token prediction (MTP) training: a residual component of the MTP cross-entropy gradient has low signal-to-noise and interferes with learning, increasingly so with scale. It decomposes the gradient into a KL self-distillation term toward the next-token distribution and this residual. It introduces a KL gradient-recovery trick using straight-through routing, yielding Soft-MTP, a one-line change that backpropagates only the KL signal. The authors report lower perplexity, faster speculative decoding, and higher zero-shot accuracy from 167M to 1.2B parameters, with no added parameters.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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The paper identifies a flaw in standard multi-token prediction (MTP) training: a residual component of the MTP cross-entropy gradient has low signal-to-noise and interferes with learning, increasingly so with scale. It decomposes the gradient into a KL self-distillation term toward the next-token distribution and this residual. It introduces a KL gradient-recovery trick using straight-through routing, yielding Soft-MTP, a one-line change that backpropagates only the KL signal. The authors report lower perplexity, faster speculative decoding, and higher zero-shot accuracy from 167M to 1.2B parameters, with no added parameters.
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Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; results are reported only up to 1.2B parameters, so scaling behavior beyond that is untested.
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Key findings from the stored brief
- The MTP cross-entropy gradient decomposes exactly into a KL self-distillation term plus a low signal-to-noise residual.
- The KL gradient-recovery trick uses straight-through gradient routing and avoids vocabulary-sized logit matrices.
- Soft-MTP improves over standard MTP at every tested scale from 167M to 1.2B parameters, per the paper's reported results.
Source claims not independently checked; results are reported only up to 1.2B parameters, so scaling behavior beyond that is untested.
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Evidence & sources
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Why this may matter
Multi-token prediction is increasingly used in pre-training for speculative decoding and downstream gains. A zero-overhead fix to its gradient could improve training efficiency and inference speed for language models.
Limits and open questions
Source claims not independently checked; results are reported only up to 1.2B parameters, so scaling behavior beyond that is untested.
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