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This research addresses the instability of gradient descent in large models over long timescales by proposing a meta-learning framework where agents optimize their own weights and biases. The approach combines high-dimensional inner-loop optimization with low-dimensional outer-loop updates, enabling the use of standard zeroth-order methods to accelerate learning processes without relying on traditional optimizers that fail at arbitrary time horizons.
Greedy dynamical meta-learning
arXiv:2607.23925v1 Announce Type: new Abstract: Gradient descent scales well to large models, but becomes unstable over long time horizons. Gradient-free optimizers can scale to arbitrary timespans, but are hobbled by high dimensions. Since learning occurs in large models over long timescales, neither of these approaches is likely to produce traits which can accelerate the learning process. Instead, we propose a meta-learning algorithm in which the agent learns to modify its own weights and biases. Our algorithm consists of an inner loop, wherein the agent performs some high-dimensional optimization upon itself, and an outer loop, wherein we perform some low-dimensional optimization upon the inner loop. Since the outer loop handles very few parameters, standard zeroth-order methods may be used.
Abstract:Gradient descent scales well to large models, but becomes unstable over long time horizons. Gradient-free optimizers can scale to arbitrary timespans, but are hobbled by high dimensions. Since learning occurs in large models over long timescales, neither of these approaches is likely to produce traits which can accelerate the learning process. Instead, we propose a meta-learning algorithm in which the agent learns to modify its own weights and biases. Our algorithm consists of an inner loop, wherein the agent performs some high-dimensional optimization upon itself, and an outer loop, wherein we perform some low-dimensional optimization upon the inner loop. Since the outer loop handles very few parameters, standard zeroth-order methods may be used.
Machine Learning (cs.LG)
arXiv:2607.23925 [cs.LG]
(or arXiv:2607.23925v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.23925 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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