Summary of Stochastic Control For Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence, by Yinbin Han et al.
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
by Yinbin Han, Meisam Razaviyayn, Renyuan Xu
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of fine-tuning diffusion models for specific downstream tasks, constraints, and human preferences. Recent advances have employed reinforcement learning algorithms, but these approaches are largely empirical, lacking theoretical understanding. The authors propose a stochastic control framework for fine-tuning diffusion models, building on denoising diffusion probabilistic models as the pre-trained reference dynamics. This framework integrates linear dynamics control with Kullback-Leibler regularization and establishes well-posedness and regularity of the stochastic control problem. A policy iteration algorithm (PI-FT) is developed for numerical solution, achieving global convergence at a linear rate. The paper also explores extensions to parametric settings and continuous-time formulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make it easier to use big models called diffusion models that are good at generating pictures or sounds. Right now, it’s hard to adjust these models for specific tasks or goals, like making a picture more realistic. The scientists propose a new way to fine-tune these models using something called stochastic control. They show that their approach works well and can be used in different situations. This could help us create more realistic pictures or sounds in the future. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Regularization » Reinforcement learning
