Summary of Reward-directed Score-based Diffusion Models Via Q-learning, by Xuefeng Gao et al.
Reward-Directed Score-Based Diffusion Models via q-Learning
by Xuefeng Gao, Jiale Zha, Xun Yu Zhou
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel reinforcement learning (RL) framework for training continuous-time score-based diffusion models in generative AI. The approach aims to generate samples that maximize reward functions while keeping the generated distributions close to unknown target data distributions. Unlike existing studies, this formulation does not rely on pretrained models for the unknown score functions of noise-perturbed data distributions. The authors develop an entropy-regularized continuous-time RL problem and show that the optimal stochastic policy has a Gaussian distribution with a known covariance matrix. They then parameterize the mean of Gaussian policies and design an actor-critic type (little) q-learning algorithm to solve the RL problem. A key component is obtaining noisy observations from the unknown score function via a ratio estimator. The authors demonstrate the effectiveness of their approach by comparing its performance with two state-of-the-art RL methods that fine-tune pretrained models. Finally, they discuss extensions to probability flow ODE implementation of diffusion models and conditional diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to train machines to generate pictures or sounds that are realistic and follow certain rules. The researchers developed a special algorithm that can learn how to make these generated samples better by trying different things and seeing what works best. This algorithm doesn’t need any pre-trained help, which makes it more powerful than other methods. They tested their approach against two popular methods and showed that it’s effective. The authors also talked about ways to use this new method in the future. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
