Low-Confidence Remasking Traps Flexibility: Realizing Arbitrary-Order Potential for Diverse Rollouts in Diffusion LLMs
This study reveals that the Low-Confidence Remasking (LCR) mechanism in diffusion large language models severely compromises generation diversity. We demonstrate that this degradation stems from the over-filtering behavior of LCR rather than the generation order itself. To address this limitation, we propose a Top-Probability Position (TPP) decoding strategy coupled with an entropy-guided initialization method, thereby restoring the flexibility of arbitrary-order generation. Experimental results indicate that our approach achieves Pass@k performance comparable to autoregressive decoding while substantially enhancing rollout diversity, solution coverage, and downstream policy optimization outcomes. By decoupling generation diversity from rigid remasking heuristics, this work establishes a new paradigm for efficient and diverse generation in diffusion language models.