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Diffusion large language models generate text in a way that does not fit cleanly into the reinforcement learning recipes built for autoregressive models, and training them to reason exposes two specific problems. Rewards are sparse, so a single terminal reward fails to guide intermediate generation steps, and policy updates sometimes drift toward unnatural trajectories rather than authentic generation paths. This paper proposes Process Aligned Policy Optimization to fix both.
Two failure modes named: The work isolates sparse rewards and trajectory drift as the core obstacles to stable RL training for reasoning in diffusion LLMs, rather than treating training instability as a black box.
Step-aware process rewards: PAPO converts terminal rewards into granular, step-level guidance, so intermediate denoising steps receive a learning signal instead of waiting for a single end-of-sequence score.
Entropy-guided re-enactment: At critical high-uncertainty moments, the method replays genuine generation paths, keeping updates aligned with how the model actually produces text instead of chasing artificial trajectories.
Why it matters: Diffusion LLMs are a serious alternative to autoregressive models, and giving them a stable RL recipe for reasoning, with reported gains from 4.5% to 42.2% on benchmarks like GSM8K and MATH500, helps close the reasoning gap between the two paradigms.
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