Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning

TL;DR

This paper introduces contrastive distillation, a self-supervised loss policy that enhances transfer learning efficiency by leveraging high mutual information between source and target tasks, outperforming traditional methods.

Contribution

The paper proposes a novel contrastive distillation method that improves transfer learning by efficiently sampling negative examples and capturing high mutual information.

Findings

Outperforms common transfer learning methods

Enables more efficient sampling of negative examples

Facilitates rapid adaptation in diverse subspaces

Abstract

Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization. While some approaches have been adopted to refine representations via auxiliary self-supervised losses while simultaneously learning decision policies, learning compositional representations from hand-designed and context-independent self-supervised losses (multi-view) still adapts relatively slowly to the real world, which contains many non-IID subspaces requiring rapid distribution shift in both time and spatial attention patterns at varying levels of abstraction. In contrast, supervised language model cascades have shown the flexibility to adapt to many diverse manifolds, and hints of self-learning needed for autonomous task transfer. However, to date, transfer methods for language models like few-shot learning and fine-tuning still require human supervision and transfer learning using self-learning methods has been underexplored. We propose a self-supervised loss policy called contrastive distillation which manifests latent variables with high mutual information with both source and target tasks from weights to tokens. We show how this outperforms common methods of transfer learning and suggests a useful design axis of trading off compute for generalizability for online transfer. Contrastive distillation is improved through sampling from memory and suggests a simple algorithm for more efficiently sampling negative examples for contrastive losses than random sampling.