Self-Correcting Self-Consuming Loops for Generative Model Training

Abstract: As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data. Despite the successful stories of using synthetic data for representation learning, using synthetic data for generative model training creates "self-consuming loops" which may lead to training instability or even collapse, unless certain conditions are met. Our paper aims to stabilize self-consuming generative model training. Our theoretical results demonstrate that by introducing an idealized correction function, which maps a data point to be more likely under the true data distribution, self-consuming loops can be made exponentially more stable. We then propose self-correction functions, which rely on expert knowledge (e.g. the laws of physics programmed in a simulator), and aim to approximate the idealized corrector automatically and at scale. We empirically validate the effectiveness of self-correcting self-consuming loops on the challenging human motion synthesis task, and observe that it successfully avoids model collapse, even when the ratio of synthetic data to real data is as high as 100%.

• The paper presents a theoretical framework that uses self-correcting functions to align synthetic data with target distributions, enhancing training stability.
• It validates the approach with human motion synthesis experiments, demonstrating maintained performance even with 100% synthetic data and reduced variance.
• The study shows that minimal self-correction effectively prevents model collapse, providing a scalable solution for robust generative model training.

Self-Correcting Self-Consuming Loops for Generative Model Training

The paper "Self-Correcting Self-Consuming Loops for Generative Model Training" presents a novel approach to enhancing the stability and effectiveness of training generative models when a significant portion of the training data is synthetic. The rapid increase in synthetic data available online poses a challenge as training models continuously on such data can lead to "self-consuming loops," resulting in model degradation or collapse. The research focuses on introducing a self-correcting mechanism to mitigate these effects.

Summary of Contributions

The authors introduce a theoretical framework to stabilize generative model training in self-consuming loops, achieved through a self-correction function. This function aims to automatically correct synthetic data, bringing it closer to the target distribution. The primary contributions can be summarized as follows:

1. Theoretical Analysis: The authors propose an idealized correction function to enhance training stability exponentially. This correction function aims to map data points to be more representative of true data distribution probabilities.
2. Self-Correction Functions: These functions are designed to approximate the ideal correction using expert knowledge such as physical laws. They are scalable and eliminate the need for human intervention.
3. Empirical Validation: The concept is tested on human motion synthesis tasks, demonstrating that self-corrected models can maintain performance even when the dataset comprises 100% synthetic data. The study confirms that model collapse can be avoided using self-correcting mechanisms.

Key Results

In the human motion synthesis experiments, the application of self-correcting functions using a physics simulator shows that:

• Models maintain high-quality output with up to 100% synthetic data in the training set.
• Self-corrected models exhibit reduced variance and improved stability in self-consuming loops compared to non-corrected models.
• The implemented self-correction successfully approximates the ideal correction function, validating its practical utility.

Theoretical Implications

The theoretical results presented in the paper highlight that self-correcting functions can greatly impact training generative models by effectively interpolating between synthetic and real-like data quality. The stability bounds derived suggest that incorporating some form of correction can exponentially improve both the stability and accuracy of iterative model updates.

The asymptotic analysis and stability proofs indicate that small amounts of idealized self-correction can significantly enhance performance, offering a systematic approach to addressing self-consumption challenges in generative models.

Practical Implications and Future Work

The proposed framework has profound practical implications, particularly in tasks where acquiring real data is difficult, and synthesized data is utilized extensively. The research demonstrates a path forward for future technological applications such as autonomous vehicles, robotics, and virtual reality simulations that rely heavily on synthetic datasets.

For future work, exploring broader applications of self-correcting functions in diverse domains like text-to-image and video generation could provide further insights. Additionally, investigating methods to robustly measure and simulate the idealized correction function across different generative model architectures could offer deeper understanding and more tailored solutions.

Overall, the paper provides a comprehensive examination of a novel solution for generative model training, addressing crucial challenges associated with synthetic data usage, and sets a foundation for robust and scalable model training methodologies with synthetic datasets.