Tilt Matching for Scalable Sampling and Fine-tuning
Published:Dec 26, 2025 02:12
•1 min read
•ArXiv
Analysis
This paper introduces Tilt Matching, a novel algorithm for sampling from unnormalized densities and fine-tuning generative models. It leverages stochastic interpolants and a dynamical equation to achieve scalability and efficiency. The key advantage is its ability to avoid gradient calculations and backpropagation through trajectories, making it suitable for complex scenarios. The paper's significance lies in its potential to improve the performance of generative models, particularly in areas like sampling under Lennard-Jones potentials and fine-tuning diffusion models.
Key Takeaways
Reference
“The algorithms do not require any access to gradients of the reward or backpropagating through trajectories of the flow or diffusion.”