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Internal Guidance as a Sampling Method for Tilted Diffusion Models

Curtis McDonald, Peter L. Bartlett · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29086 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Os métodos de orientação binária de recompensa mostram uma inclinação exponencial de uma densidade de base usando um modelo de difusão treinado com apenas amostras da distribuição de base. Os métodos existentes usam o que chamamos de orientação externa: termos adicionais na deriva ou termos multiplicativos sobre probabilidades de transição. Propomos orientação interna que deixa a saída do denoiser de base inalterada e, em vez disso, altera o ponto em que é avaliado. Para distribuições discretas incorporadas no hipercube $\{-1,1\}^{d}$, o denoiser de qualquer densidade inclinada linearmente é exatamente o denoiser de base avaliado em uma entrada deslocada e reescalada. Na prática, o denoiser de base é uma rede neural treinada em nenhuma amostra da distribu

Machine translation (Meta M2M100); the English original follows.

Abstract

Reward guidance methods sample an exponential tilt of a base density using a diffusion model trained with only samples from the base distribution. Existing methods use what we call external guidance: additive terms in the drift or multiplicative terms on the transition probabilities. We propose internal guidance which leaves the output of the base denoiser unmodified and instead changes the point at which it is evaluated. For discrete distributions embedded in the hypercube $\{-1,1\}^{d}$, the denoiser of any linearly tilted density is exactly the base denoiser evaluated at a shifted and rescaled input. In practice the base denoiser is a neural network trained on noised samples of the base distribution. The required evaluation points for sampling the tilted density would be out-of-distribution for basic noising of the base distribution. We modify the training algorithm with random diagonal rescalings of the data, so that the denoiser network learns both in- and out-of-distribution evaluations of the denoiser relative to the base distribution. A single denoiser network samples binary reward tilts, class labels, and infilling with no fine-tuning and no external guidance term.

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Topics: Base (topology), Algorithm, Mathematics, Diagonal, Diffusion, Point (geometry)

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