arXiv:2607.07156v1 Announce Type: new Abstract: Different optimizers have different update biases, but these biases are usually implicit. Existing studies mainly analyze or control such biases from the geometry of the final solution. However, how optimizer bias forms during training still lacks a clear internal mechanism. This paper proposes an information allocation dynamics perspective. It interprets optimizer implicit bias as the relative allocation of training signals between weight-like and bias-like parameter pathways. This allocation can be described and adjusted by a continuous precond
This research provides a deeper, mechanistic understanding of optimizer bias in neural networks, moving beyond post-hoc analysis of final solutions.
A clearer understanding of optimizer behavior can lead to more efficient and predictable AI training, accelerating model development and improving performance.
The explicit description and adjustment of optimizer bias via 'information allocation dynamics' offers a new leverage point for AI researchers and practitioners.
- · AI Researchers
- · Deep Learning Framework Developers
- · Compute Infrastructure Providers
- · Organizations relying on brute-force hyperparameter tuning
Improved understanding and control over neural network training dynamics.
Development of more robust, efficient, and specialized AI optimizers.
Faster deployment of advanced AI models across various applications, reducing the time and computational cost of AI innovation.
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