arXiv:2601.21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-SSL) have struggled to build functional representations in deep neural networks. To establish a link between global and local rules, we first develop a theory for deep linear networks: we identify conditions for local-SSL algorithms (like Forward-forward or CLAPP) to implement exactly the same weight update as a global BP-SSL. Starting from the theoretical insights,
This research provides a theoretical framework to bridge the gap between global and local learning rules, an ongoing challenge in deep learning that could accelerate AI development.
A strategic reader should care because improvements in local learning algorithms could significantly reduce computational costs and complexity for training large AI models, impacting the entire AI development ecosystem.
The potential to match or surpass backpropagation with more biologically plausible and computationally efficient local learning methods could fundamentally change how AI models are designed and scaled.
- · AI researchers
- · Hardware manufacturers
- · AI development platforms
- · High-energy-consumption data centers
- · Traditional backpropagation-reliant AI startups
More efficient AI training algorithms become available for a broader range of applications.
Reduced computational demand could lower barriers to entry for AI development and democratize advanced AI capabilities.
Energy consumption from AI training could decrease, alleviating some pressure on the energy grid and contributing to more sustainable AI development.
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Read at arXiv cs.LG