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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.03234 |
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| _version_ | 1866917121143865344 |
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| author | Pachebat, Jean Conforti, Giovanni Durmus, Alain Janati, Yazid |
| author_facet | Pachebat, Jean Conforti, Giovanni Durmus, Alain Janati, Yazid |
| contents | We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt $\exp(λr)$ into $N$ sequential smaller tilts, each admitting a tractable score update via first-order Taylor expansion. This requires only forward evaluations of the reward function and avoids backpropagating through sampling chains. We validate on a two-dimensional Gaussian mixture with linear reward, where the exact tilted distribution is available in closed form. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative Tilting for Diffusion Fine-Tuning Pachebat, Jean Conforti, Giovanni Durmus, Alain Janati, Yazid Machine Learning We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt $\exp(λr)$ into $N$ sequential smaller tilts, each admitting a tractable score update via first-order Taylor expansion. This requires only forward evaluations of the reward function and avoids backpropagating through sampling chains. We validate on a two-dimensional Gaussian mixture with linear reward, where the exact tilted distribution is available in closed form. |
| title | Iterative Tilting for Diffusion Fine-Tuning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.03234 |