SOURCE-LINKED INTELLIGENCE
Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models
Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mechanism, DLMs operate over an exponentially large exploration space compared to autoregressive models (ARMs), making it challenging to focus on reasoning-guiding tokens under random masking. We define causal shortcuts as token chains that cover the full sequence and provide explicit guidance towards correct reasoning trajectories. We analyze the effects of causal shortcuts on the reasoning accuracy and convergence speed of DLMs, and find that they
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T15:27:05.000Z
First collected: 2026-09-24T08:22:30.429Z. This is not the publication date.