The paper "Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences" by Damien Ferbach, Quentin Bertrand, Avishek Joey Bose, and Gauthier Gidel explores the impact of data curation on the training of generative models. The authors focus on the self-consuming loop, where generated data is used to retrain the model, and investigate how this process can be seen as an implicit optimization of human preferences. Key contributions of the paper include: 1. **Theoretical Analysis**: The authors provide theoretical results showing that the expected reward underlying the curation process increases and its variance collapses. They also prove convergence to the maximum reward regions. 2. **Stability of Iterative Retraining**: They study the stability of the iterative retraining loop when real data is injected at each step, improving previous results and showing that the KL divergence with the optimal distribution remains bounded. 3. **Connection to RLHF**: The paper highlights connections between the retraining process and Reinforcement Learning from Human Feedback (RLHF), demonstrating that the expected reward increases and the KL divergence remains controlled. The authors conduct experiments on synthetic datasets and CIFAR10 to illustrate their theoretical findings. They show that retraining on curated synthetic samples leads to a collapse to regions that maximize the reward, while the use of real data improves stability and increases the expected reward. Additionally, they highlight that retraining on a mixture of real and curated samples can lead to increased bias, particularly when the reward is implicitly correlated with class labels. The paper concludes by discussing the broader impacts of their findings, emphasizing the ethical concerns related to the alignment of large generative models with human preferences and the potential for bias amplification.The paper "Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences" by Damien Ferbach, Quentin Bertrand, Avishek Joey Bose, and Gauthier Gidel explores the impact of data curation on the training of generative models. The authors focus on the self-consuming loop, where generated data is used to retrain the model, and investigate how this process can be seen as an implicit optimization of human preferences. Key contributions of the paper include: 1. **Theoretical Analysis**: The authors provide theoretical results showing that the expected reward underlying the curation process increases and its variance collapses. They also prove convergence to the maximum reward regions. 2. **Stability of Iterative Retraining**: They study the stability of the iterative retraining loop when real data is injected at each step, improving previous results and showing that the KL divergence with the optimal distribution remains bounded. 3. **Connection to RLHF**: The paper highlights connections between the retraining process and Reinforcement Learning from Human Feedback (RLHF), demonstrating that the expected reward increases and the KL divergence remains controlled. The authors conduct experiments on synthetic datasets and CIFAR10 to illustrate their theoretical findings. They show that retraining on curated synthetic samples leads to a collapse to regions that maximize the reward, while the use of real data improves stability and increases the expected reward. Additionally, they highlight that retraining on a mixture of real and curated samples can lead to increased bias, particularly when the reward is implicitly correlated with class labels. The paper concludes by discussing the broader impacts of their findings, emphasizing the ethical concerns related to the alignment of large generative models with human preferences and the potential for bias amplification.