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arXiv cs.LG
arXiv cs.LG
5/13/2026
LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection

LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection

Short summary

LEAP is a training-free method that identifies tokens converging early during diffusion language model denoising, reducing denoising steps by ~30% and enabling 7.2 tokens per step on GSM8K while preserving accuracy. The technique breaks reliance on high confidence thresholds, enabling faster parallel decoding without retraining.

  • •LEAP detects early-converging tokens in diffusion LLM denoising via future context filtering and multi-sequence superposition
  • •Reduces denoising steps ~30% compared to confidence-based methods while maintaining model precision
  • •Achieves 7.2 tokens per step on benchmarks, making parallel decoding practical without confidence constraints

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Read full article at arXiv cs.LG

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