Efficient and Stable Reinforcement Learning for Diffusion Language Models

• URL: http://arxiv.org/abs/2602.08905v1
• Date: Mon, 09 Feb 2026 17:04:23 GMT
• Title: Efficient and Stable Reinforcement Learning for Diffusion Language Models
• Authors: Jiawei Liu, Xiting Wang, Yuanyuan Zhong, Defu Lian, Yu Yang,
• Abstract summary: Reinforcement Learning (RL) is crucial for unlocking the complex reasoning capabilities of Diffusion-based Large Language Models (dLLMs)<n>Applying to dLLMs faces unique challenges in efficiency and stability.<n>We propose Spatio-Temporal Pruning (STP), a framework designed to simultaneously improve the efficiency and stability of RL for dLLMs.
• Score: 59.75789436018925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) is crucial for unlocking the complex reasoning capabilities of Diffusion-based Large Language Models (dLLMs). However, applying RL to dLLMs faces unique challenges in efficiency and stability. To address these challenges, we propose Spatio-Temporal Pruning (STP), a framework designed to simultaneously improve the efficiency and stability of RL for dLLMs. STP compresses the redundancy in the generative process through: (1) \textit{spatial pruning}, which constrains the exploration space using static priors; and (2) \textit{temporal pruning}, which bypasses redundant late-stage refinement steps. Our theoretical analysis demonstrates that STP strictly reduces the variance of the log-likelihood estimation, thereby ensuring more stable policy updates. Extensive experiments demonstrate that STP surpasses state-of-the-art baselines in both efficiency and accuracy. Our code is available at https://github.com/Lolo1222/STP.

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