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arXiv · 2610.18200

A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs

Jiahao Kuang·Yangfan Gao·Tao Gui·Yuanbin Wu·Qi Zhang·Xuanjing Huang·Tao Ji
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Abstract

Parallel generation is widely regarded as an inherent advantage of diffusion large language models. Yet our fixed-quota evaluation of four representative models reveals severe degradation across mathematics and code, with all scores below 15% at 16 tokens per step. Token-level probes identify local predictive potential near the observed prefix, especially in mathematical text. We propose RePara, which reinforces short consecutive token blocks through group-relative policy optimization and reduces rollout and log-probability recomputation costs. On LLaDA and SDAR, \ours improves GSM8K and MATH-500 accuracy over JustGRPO under reduced denoising budgets, with a 1.96x training-step speedup in our LLaDA/MATH setup.

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Jiahao Kuang, Yangfan Gao, Tao Gui, Yuanbin Wu, Qi Zhang, Xuanjing Huang, Tao Ji. 2026-10-13. A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs. https://arxiv.org/abs/2610.18200

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