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arXiv · 2610.03641

IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models

Vladislav Gromadskii·David Li·Samson Gourevitch·Yazid Janati·Eric Moulines·Maxim Panov·Alexander Korotin
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Abstract

Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable sequence likelihoods complicate reward fine-tuning. We introduce IDRF, a framework for reward fine-tuning of few-step masked discrete diffusion generators. Starting from a standard reverse-KL-regularized objective, IDRF replaces the intractable sequence-level KL penalty with inverse-distillation regularization. With an optimal auxiliary denoiser, we prove that the population inverse-distillation loss upper-bounds the sequence-level KL divergence to the reference distribution. IDRF optimizes a trajectory-based surrogate of this loss without reference-model rollouts, so the student keeps its own few-step sampler. We view few-step generation as a finite-horizon Markov decision process and optimize reward with a clipped policy-gradient objective over the student's trajectories. Across DNA, image, and text generation, IDRF achieves high reward with up to $32\times$ fewer denoising steps than the reference while mitigating reward hacking and preserving sample quality.

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Vladislav Gromadskii, David Li, Samson Gourevitch, Yazid Janati, Eric Moulines, Maxim Panov, Alexander Korotin. 2026-10-02. IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models. https://arxiv.org/abs/2610.03641

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