Title: DiFFPO: Training Diffusion LLMs to Reason Fast and Furious via Reinforcement Learning
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Diffusion Language Models, Efficient Reasoning

Abstract: We propose **DiFFPO**, **Di**ffusion **F**ast and **F**urious **P**olicy **O**ptimization, a unified framework for training masked diffusion large language models (dLLMs) to reason not only *better (furious)*, but also *faster* via reinforcement learning (RL). We first generalize the existing baseline approach such as d1 by proposing to train surrogate policies via off-policy RL, whose likelihood is much more tractable as an approximation to the true dLLM policy. This naturally motivates a more accurate two-stage likelihood approximation combined with importance sampling correction, which leads to generalized RL algorithms with better sample efficiency and superior task performance. Second, we propose a new direction of training efficient samplers/controllers of dLLMs policy. Via RL, we incentivize dLLMs' natural multi-token prediction capabilities by letting the model learn to adaptively allocate an inference threshold for each prompt, which yields better accuracies with lower number of function evaluations (NFEs) compared to the base model. Finally, we consider joint training of the dLLM policy and the sampler together to obtain the best performance in improving the Pareto frontier of the inference-time compute of dLLMs. We showcase the effectiveness of our pipeline by training open source large diffusion language models over math and planning benchmark tasks.