Empirical Analysis of Decoding Biases in Masked Diffusion Models

Masked Diffusion Models (MDMs) have recently emerged as a promising nonautoregressive paradigm for sequence generation. However, their performance is highly sensitive to the choice of decoding strategy. In this work, we reveal that prevalent uncertainty-based decoding strategies induce two decoding biases in MDMs: rigid boundary bias and trivial token bias. These biases limit the model's reasoning ability and ultimately degrade generation quality. To address these challenges, we propose UNmasking Calibration for DecOding DEbiasing (UNCODE), a decoding calibration framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness. Extensive experiments on three advanced MDMs across seven reasoning-and planning-intensive benchmarks demonstrate that UNCODE consistently outperforms existing decoding strategies by more than 7%, while achieving performance comparable to autoregressive models of similar parameter scales. All codes are available at https://github.com/NEUIR/Uncode .