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  "text": "MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Models\n\n\"We propose a novel Masked Diffusion Policy Optimization (MDPO) to  exploit the Markov property diffusion possesses and explicitly train the  model under the same progressive refining schedule used at inference.  MDPO matches the performance of the previous state-of-the-art (SOTA)  method with 60x fewer gradient updates, while achieving average  improvements of 9.6% on MATH500 and 54.2% on Countdown over SOTA when  trained within the same number of weight updates. Additionally, we  improve the remasking strategy of MDLMs as a plug-in inference  replacement to overcome the limitation that the model cannot refine  tokens flexibly. This simple yet effective training-free strategy, what  we refer to as RCR, consistently improves performance and yields  additional gains when combined with MDPO\"",
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