Pruning Neural Networks as a Game: An Equilibrium Approach
Published:Dec 26, 2025 18:25
•1 min read
•ArXiv
Analysis
This paper introduces a novel perspective on neural network pruning, framing it as a game-theoretic problem. Instead of relying on heuristics, it models network components as players in a non-cooperative game, where sparsity emerges as an equilibrium outcome. This approach offers a principled explanation for pruning behavior and leads to a new pruning algorithm. The focus is on establishing a theoretical foundation and empirical validation of the equilibrium phenomenon, rather than extensive architectural or large-scale benchmarking.
Key Takeaways
Reference
“Sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium.”