[Paper] Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models

Source: arXiv - 2606.12273v1

Overview

Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random masking strategies that overlook intrinsic token dependencies. In this work, we present an empirical analysis of attention in dLLMs and show that tokens attending more strongly to unmasked context exhibit greater generation stability and play a critical role in reasoning. Motivated by these findings, we propose AGDO, an attention-guided denoising and optimization framework that aligns both training and optimization with attention-derived dependencies. AGDO determines the denoising order based on attention structure and emphasizes attention-critical tokens during supervised fine-tuning and reinforcement learning. Experiments on mathematical and coding benchmarks demonstrate that AGDO consistently improves reasoning performance, outperforming state-of-the-art post-training methods for dLLMs.

Key Contributions

This paper presents research in the following areas:

• cs.CL

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CL.

Authors

• Jia Deng
• Junyi Li
• Wayne Xin Zhao
• Jinpeng Wang
• Hongyu Lu
• Ji-Rong Wen

Paper Information

• arXiv ID: 2606.12273v1
• Categories: cs.CL
• Published: June 10, 2026
• PDF: Download PDF