arXiv:2605.25395v1 Announce Type: new Abstract: Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning training mainly due to stochastic gradient noise and non-convex loss landscapes. In particular, standard lookahead relies on short-horizon update signals (e.g., differences between consecutive iterates), which are inherently noisy and can lead to unstable extrapolation directions. This work revisits Nesterov's acceleration from a trajectory perspective and argues that effective acceleration in deep
The continuous drive for more efficient deep learning training algorithms necessitates improvements to fundamental optimization techniques like Nesterov's momentum.
Improved optimization algorithms can lead to faster and more stable development of AI models, lowering compute costs and accelerating research.
This research introduces a method to stabilize Nesterov's lookahead, potentially making advanced optimization techniques more robust and widely applicable in deep learning.
- · AI researchers and developers
- · Cloud computing providers
- · Deep learning application developers
- · Inefficient AI training methods
More stable and faster training of complex deep learning models becomes possible.
Reduced computational resources needed for model training could lower the barrier to entry for AI development.
Accelerated AI development across various sectors could lead to faster deployment of AI-driven solutions and services.
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Read at arXiv cs.LG