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Soft-MTP paper reveals KL self-distillation signal hidden in standard Multi-Token Prediction gradients

·2026.10.31 03:53

Key point

The method uses a one-line code change to extract a hidden KL self-distillation signal from MTP gradients, improving performance across a range of tasks.

Details

  • Standard Multi-Token Prediction (MTP) gradients hide a KL self-distillation signal behind a noisy residual that harms next-token prediction.
  • A proposed "KL gradient-recovery trick" extracts the KL gradient using a one-line code change: h_ntp += h_mtp.detach() - h_mtp.
  • Soft-MTP is described as a "free lunch" that improves MTP across a range of tasks.
  • Resources include code on GitHub, a paper on arXiv (2610.38331), and a Hugging Face collection.
speculative-decodingmulti-token-predictionllm-trainingresearch-papergradient optimization
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