RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance

Diffusion Large Language Models (dLLMs) have shown great potential in language modeling, yet enhancing their capacity for complex reasoning remains a critical challenge. For autoregressive language models, this is typically addressed by guiding the reasoning process step by step using Process Reward Models (PRMs), which necessitate dense annotation for intermediate steps. However, this approach cannot be applied to dLLMs, since their intermediate generations are partially masked, non-sequential states rather than complete prefixes. Here we propose Reward-Free Guidance (RFG), a training-free framework that guides the reasoning trajectory of dLLMs without explicit process reward models. We provide theoretical justification that a process reward for partially masked states can be parameterized by the log-likelihood ratio of a policy and a reference model, which can be instantiated with off-the-shelf dLLM checkpoints without additional training. Extensive experiments demonstrate that RFG consistently outperforms various state-of-the-art post-trained dLLM baselines, achieving absolute accuracy gains of up to 9.2%.