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Monday, October 26, 2026 · Past edition · 13 stories

Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.

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Model releases & benchmarks
Model releases & benchmarks
Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models
An arXiv abstract introduces dSpark, which learns when to reveal tokens to speed parallel generation in diffusion language models.
Why it matters: For dLLM builders, dSpark targets decoding-round efficiency by scheduling commits for their downstream predictive value, not only token-level ease. The abstract reports gains on GSM8K, but provides limited detail on baselines and quality measurements.
Original · arxiv.org (new tab)Read

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Model releases & benchmarks

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

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