Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models
Changes how outcome rewards weight decisions at different denoising states during diffusion-model reinforcement learning. Derives the effect for binary rewards and uses matched-scale controls to test whether the schedule improves mathematical accuracy and reasoning-trace diversity.
First public: · Added to this list: October 18, 2026
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arXiv:2610.00661 · Initial submission: 2026-09-30
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Alternative generation methods — Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.
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