Diffusion large language models (dLLMs) are emerging as an efficient alternative to autoregressive models due to their ability to decode multiple tokens in parallel. However, aligning dLLMs with human preferences or task-specific rewards via reinforcement learning (RL) is challenging because their intractable log-likelihood precludes the direct application of standard policy gradient methods. While prior work uses surrogates like the evidence lower bound (ELBO), these one-sided approximations can introduce significant policy gradient bias. To address this, we propose the Sandwiched Policy Gradient (SPG) that leverages both an upper and a lower bound of the true log-likelihood. Experiments show that SPG significantly outperforms baselines based on ELBO or one-step estimation. Specifically, SPG improves the accuracy over state-of-the-art RL methods for dLLMs by 3.6% in GSM8K, 2.6% in MATH500, 18.4% in Countdown and 27.0% in Sudoku.
@article{arxiv.2510.09541,
title = {SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models},
author = {Chenyu Wang and Paria Rashidinejad and DiJia Su and Song Jiang and Sid Wang and Siyan Zhao and Cai Zhou and Shannon Zejiang Shen and Feiyu Chen and Tommi Jaakkola and Yuandong Tian and Bo Liu},
journal= {arXiv preprint arXiv:2510.09541},
year = {2026}
}