TIPS: A Tiny Inference-time Policy for Scaling in Diffusion Language Models

Masked diffusion language models expose an inference-time choice: which positions become visible after each denoising pass. Many existing decoding methods use hand-designed rules or require visibility to grow monotonically. We present a T iny I nference-time P olicy for S caling (TIPS), an approximately 600 K-parameter controller that learns both unmasking and remasking while keeping the underlying language model frozen. TIPS uses visibility-aware confidence features and a shallow transformer to select which tokens should be visible, while a fixed schedule controls the expected visible fraction. We train the controller through supervised warmup followed by reinforcement learning from terminal correctness rewards. On GSM8K and MATH-500 with LLaDA-8B-Instruct, the full-canvas and two-block TIPS variants achieve 58.46% and 57.97% micro-averaged accuracy at a budget of 32 decoder evaluations, exceeding the strongest baseline in our main comparison by 12.1 and 11.6 percentage points, respectively. Both variants are trained at a budget of 32 evaluations and remain competitive at higher budgets without retraining. These results demonstrate that learning when to reveal and reconsider tokens can improve the accuracy-compute trade-off without updating the decoder backbone.