Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models
Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constrained by a dual misalignment between authentic generation trajectory and the gradient update process: (i) Process-reward misalignment. Sparse, terminal rewards are indiscriminately assigned to all intermediate steps of the generation process, failing to provide discriminative credit assignment. (ii) State-trajectory misalignment. Policy updates are often diverted toward artificial, out-of-trajectory states, squandering gradients on less informative samples. To address these limitations, we introduce Process Aligned ...
| Authors | Yawen Shao, Jie Xiao, Kai Zhu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 6 September 2026 |
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