Predator–Prey Model: Driven Hunt for Accelerated Grokking
Abstract
A machine learning method is proposed using two agents (representing two coupled replicas of the model parameters) that simulate the biological behavior of a predator and a prey. In this method, the two agents interact with each other via a pair of potentials that simulate the dynamics when the predator chases the prey while the prey runs away from the predator — to perform an optimization on the landscape. This method allows, for optimization landscapes with narrow ravines with gentle slope or even flat bottoms, to avoid optimization getting stuck in a ravine. For some examples of grokking (i.e., delayed generalization) problems we show that this method allows for achieving up to a hundred times faster learning in gradient evaluations and up to 60 times in the wall-clock time compared to the standard learning procedure.
Fields of Study & Subjects
Evolutionary Game Theory and Cooperation
Evolution and Genetic Dynamics
Publication details
- Journal / source
- IEEE Transactions on Artificial Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 7
- Issue
- 10
- Pages
- 1–10
- Publication date
- 1 January 2026
- Language
- EN