A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.18200 [cs.LG]

(or arXiv:2610.18200v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.18200 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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