Learning Length Priors for Canvas Allocation in Masked Diffusion Language Models

Masked diffusion language models generate responses by progressively denoising a preallocated canvas of masked tokens. A globally fixed canvas can constrain responses that need more space or waste computation on shorter ones. Recent methods address this issue by dynamically adjusting the response canvas during denoising, but the required canvas length is still unknown before denoising begins. We find that the information predictive of reference response length could be extracted from prompt representations in dLLMs before denoising begins. We propose CALP, an approach to Canvas Allocation with Learned Priors derived from frozen dLLM prompt representations. A lightweight predictor determines the canvas length before denoising, leaving the backbone and denoising procedure unchanged. We evaluate our approach on LLaDA-8B-Instruct, LLaDA-1.5, and Dream-7B-Instruct across five benchmarks covering mathematical reasoning, coding, and instruction following, against baselines with a fixed canvas and adaptive methods including DAEDAL and . Compared with DAEDAL, our method improves the average task score by 1.43 percentage points, reduces the average number of allocated response positions by 55.5%, and achieves an observed inference speedup of 3.75$\times$. Relative to $\rho$-EOS, it improves the average task score by 2.21 percentage points and achieves an observed inference speedup of 1.83$\times$.