✨ TL;DR
This paper shows that randomly initialized neural networks can learn useful representations through simple peer-to-peer consensus (self-distillation) alone, without projectors, predictors, or pretext tasks. The findings suggest that self-distillation itself is a key mechanism driving learning in self-supervised methods, independent of other architectural components.
State-of-the-art self-supervised learning methods, particularly self-distilled approaches, achieve impressive performance but rely on complex ensembles of mechanisms with many empirically motivated design choices that lack theoretical understanding. It remains unclear which components are essential for learning and which are auxiliary. Specifically, the role of self-distillation itself within the learning dynamics is not well isolated or understood, as it is typically bundled with projectors, predictors, momentum encoders, and various pretext tasks.
The authors isolate the effect of self-distillation by creating a minimal experimental setup that removes all common auxiliary components. They train a group of randomly initialized networks that learn solely through peer-to-peer consensus, where networks distill knowledge from each other without projectors, predictors, or pretext tasks. This stripped-down approach allows them to study the pure effect of self-distillation on learning dynamics. They evaluate the learned representations on downstream tasks and analyze how performance varies with different hyperparameters to understand what the models learn under these minimal conditions.
What the paper shows.
The minimal peer-to-peer consensus setup with randomly initialized networks produces representations that achieve non-trivial improvements over random baselines on downstream tasks. The learned representations demonstrate that self-distillation alone can drive meaningful learning. The performance varies with different hyperparameter choices, indicating that the strength of the self-distillation effect depends on training configuration. The analysis reveals specific patterns in what the models learn under this stripped-down setup, though the paper focuses on demonstrating the existence of the effect rather than achieving state-of-the-art performance.
The paper focuses on isolating and demonstrating the self-distillation effect rather than achieving competitive performance with full self-supervised methods, so the absolute performance of the minimal setup is likely lower than state-of-the-art approaches. The analysis of what is being learned is described as short, suggesting limited depth in understanding the learned representations. The work does not fully explain the theoretical mechanisms behind why peer-to-peer consensus alone enables learning. The generalizability across different network architectures, datasets, and scales is not extensively explored. The paper also does not provide clear guidance on how these insights should inform the design of practical self-supervised learning systems.
✨ Generated by Claude · Apr 21, 2026 · Read the PDF for authoritative content.