Summary of Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control, by Carles Domingo-enrich et al.
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
by Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 Dynamical generative models, such as Flow Matching and denoising diffusion models, have been widely used but lacked theoretically-sound methods for improving them with reward fine-tuning. This work casts reward fine-tuning as stochastic optimal control (SOC) and proves that a specific memoryless noise schedule must be enforced during fine-tuning to account for the dependency between noise and generated samples. The authors propose Adjoint Matching, an algorithm that outperforms existing SOC methods by casting SOC problems as regression problems. This approach significantly improves upon existing reward fine-tuning methods, achieving better consistency, realism, and generalization to unseen human preference reward models while retaining sample diversity. The work demonstrates the effectiveness of this novel framework for dynamical generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a computer program that can create new pictures or sounds based on some rules. But sometimes these programs don’t produce what we want, so we need to make them better. In this paper, researchers came up with a new way to improve these programs by giving them a goal, like making sure the generated samples look more realistic. They showed that their method works well and can create images or sounds that are closer to what humans would prefer. This is important because it helps us understand how we can make artificial intelligence better at creating things that are similar to what we see in real life. |
Keywords
* Artificial intelligence * Diffusion * Fine tuning * Generalization * Regression
