Summary of Fine-tuning Of Continuous-time Diffusion Models As Entropy-regularized Control, by Masatoshi Uehara et al.
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
by Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Tommaso Biancalani, Sergey Levine
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a new approach to finetuning diffusion models for tasks such as image generation or protein design. While diffusion models excel at capturing complex data distributions, they can be limited by their training objectives and may not always produce high-quality samples. The authors argue that this limitation is due to “reward collapse,” where the model becomes overly optimized towards an imperfect reward function. To address this issue, they frame the finetuning problem as entropy-regularized control against the pretrained diffusion model, using neural SDEs to optimize entropy-enhanced rewards. Their framework demonstrates efficient generation of diverse samples with high genuine rewards, mitigating overoptimization of imperfect reward models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper solves a big problem in artificial intelligence called “reward collapse.” When we try to make AI systems create better things, like images or proteins, they often get stuck because the system is too good at following bad instructions. The authors invented a new way to teach these AI systems to be creative and diverse by giving them a reward that encourages them to explore different ideas. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Image generation
