# Denoising Token Credit Assignment

Diffusion language models commit tokens over many denoising steps, so a whole-response reward says little about which tokens helped later refinement. DTCA estimates each token's contribution from the confidence gain at still-masked positions after that token is committed, averages these signals by part-of-speech category into stable weights, and uses them to pick denoising timesteps and assign token-level learning signals, with entropy-based calibration. On LLaDA-8B-Instruct it beats the base model at generation budgets of 128, 256 and 512, by 9.0, 6.8 and 4.8 points on MATH500 and 5.3, 2.1 and 2.5 points on GSM8K.

Diffusion language models commit tokens over many denoising steps, so a whole-response reward says little about which tokens helped later refinement. DTCA estimates each token's contribution from the confidence gain at still-masked positions after that token is committed, averages these signals by part-of-speech category into stable weights, and uses them to pick denoising timesteps and assign token-level learning signals, with entropy-based calibration. On LLaDA-8B-Instruct it beats the base model at generation budgets of 128, 256 and 512, by 9.0, 6.8 and 4.8 points on MATH500 and 5.3, 2.1 and 2.5 points on GSM8K.