Categorical Reparameterization with Denoising Diffusion models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908822037069824 |
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| author | Gourevitch, Samson Durmus, Alain Moulines, Eric Olsson, Jimmy Janati, Yazid |
| author_facet | Gourevitch, Samson Durmus, Alain Moulines, Eric Olsson, Jimmy Janati, Yazid |
| contents | Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging due to the non-differentiability of categorical sampling. A common workaround is to replace the discrete distribution with a continuous relaxation, yielding a smooth surrogate that admits reparameterized gradient estimates via the reparameterization trick. Building on this idea, we introduce ReDGE, a novel and efficient diffusion-based soft reparameterization method for categorical distributions. Our approach defines a flexible class of gradient estimators that includes the Straight-Through estimator as a special case. Experiments spanning latent variable models and inference-time reward guidance in discrete diffusion models demonstrate that ReDGE consistently matches or outperforms existing gradient-based methods. The code will be made available at https://github.com/samsongourevitch/redge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00781 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Categorical Reparameterization with Denoising Diffusion models Gourevitch, Samson Durmus, Alain Moulines, Eric Olsson, Jimmy Janati, Yazid Machine Learning Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging due to the non-differentiability of categorical sampling. A common workaround is to replace the discrete distribution with a continuous relaxation, yielding a smooth surrogate that admits reparameterized gradient estimates via the reparameterization trick. Building on this idea, we introduce ReDGE, a novel and efficient diffusion-based soft reparameterization method for categorical distributions. Our approach defines a flexible class of gradient estimators that includes the Straight-Through estimator as a special case. Experiments spanning latent variable models and inference-time reward guidance in discrete diffusion models demonstrate that ReDGE consistently matches or outperforms existing gradient-based methods. The code will be made available at https://github.com/samsongourevitch/redge. |
| title | Categorical Reparameterization with Denoising Diffusion models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.00781 |