← Back to all news
arXiv Machine Learning By Curtis McDonald, Peter Bartlett

Internal Guidance as a Sampling Method for Tilted Diffusion Models

Read the original on arXiv Machine Learning →

The paper introduces internal guidance as a new sampling technique for tilted diffusion models, which modifies the evaluation point of the base denoiser rather than altering its output. For discrete distributions in the hypercube, the denoiser of a linearly tilted density can be obtained by evaluating the base denoiser at a shifted and rescaled input. By augmenting training with random diagonal rescalings, a single denoiser network can sample binary reward tilts, class labels, and infilling without fine‑tuning or external guidance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Oct 15

Correction Training for Diffusion Models

arXiv:2610.14136v2 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with no...

By Donghoon Ahn, Runqian Wang, Alexei A. Efros, Taesung Park, Yash Belhe
diffusionsafety
More like this →
arXiv Machine Learning
Aug 31

Improved off-policy training of diffusion samplers

The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.

By Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Esmeralda S. Whitammer
diffusionbenchmarks
More like this →