A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs
Parallel generation is widely regarded as an inherent advantage of diffusion large language models.
ProofPaper ↗
Key points
- 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.
Sources (1)
- [1]A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:37 AM
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.
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