Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods

Oussama Zekri; Nicolas Boullé

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arXiv · 2502.01384

Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods

Oussama Zekri·Nicolas Boullé

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Abstract

Discrete diffusion models have recently gained significant attention due to their ability to process complex discrete structures for language modeling. However, fine-tuning these models with policy gradient methods, as is commonly done in Reinforcement Learning from Human Feedback (RLHF), remains a challenging task. We propose an efficient, broadly applicable, and theoretically justified policy gradient algorithm, called Score Entropy Policy Optimization (\SEPO), for fine-tuning discrete diffusion models over non-differentiable rewards. Our numerical experiments across several discrete generative tasks demonstrate the scalability and efficiency of our method. Our code is available at https://github.com/ozekri/SEPO.

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BibTeX RIS

Oussama Zekri, Nicolas Boullé. 2025-02-03. Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods. https://arxiv.org/abs/2502.01384

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