Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks

• URL: http://arxiv.org/abs/2512.22106v1
• Date: Fri, 26 Dec 2025 18:25:38 GMT
• Title: Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks
• Authors: Zubair Shah, Noaman Khan,
• Abstract summary: We show that sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium.<n>We derive a simple equilibrium-driven pruning algorithm that jointly updates network parameters and participation variables without relying on explicit importance scores.
• Score: 1.1458853556386799
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspective: pruning as an equilibrium outcome of strategic interaction among model components. We model parameter groups such as weights, neurons, or filters as players in a continuous non-cooperative game, where each player selects its level of participation in the network to balance contribution against redundancy and competition. Within this formulation, sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium. We analyze the resulting game and show that dominated players collapse to zero participation under mild conditions, providing a principled explanation for pruning behavior. Building on this insight, we derive a simple equilibrium-driven pruning algorithm that jointly updates network parameters and participation variables without relying on explicit importance scores. This work focuses on establishing a principled formulation and empirical validation of pruning as an equilibrium phenomenon, rather than exhaustive architectural or large-scale benchmarking. Experiments on standard benchmarks demonstrate that the proposed approach achieves competitive sparsity-accuracy trade-offs while offering an interpretable, theory-grounded alternative to existing pruning methods.

Related papers

• Efficient Thought Space Exploration through Strategic Intervention [54.35208611253168]
  We propose a novel Hint-Practice Reasoning (HPR) framework that operationalizes this insight through two synergistic components.<n>The framework's core innovation lies in Distributional Inconsistency Reduction (DIR), which dynamically identifies intervention points.<n> Experiments across arithmetic and commonsense reasoning benchmarks demonstrate HPR's state-of-the-art efficiency-accuracy tradeoffs.
  arXiv  Detail & Related papers  (2025-11-13T07:26:01Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• On Tractable $Φ$-Equilibria in Non-Concave Games [53.212133025684224]
  We study tractable $Phi$-equilibria in non-concave games.<n>We show that when $Phi$ is finite, there exists an efficient uncoupled learning algorithm that converges to the corresponding $Phi$-equilibria.
  arXiv  Detail & Related papers  (2024-03-13T01:51:30Z)
• Exploiting hidden structures in non-convex games for convergence to Nash equilibrium [62.88214569402201]
  A wide array of modern machine learning applications can be formulated as non-cooperative Nashlibria. We provide explicit convergence guarantees for both deterministic and deterministic environments.
  arXiv  Detail & Related papers  (2023-12-27T15:21:25Z)
• Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks [83.28949556413717]
  We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. We model players' strategies using artificial neural networks. This paper is the first to solve general continuous-action games with unrestricted mixed strategies and without any gradient information.
  arXiv  Detail & Related papers  (2022-11-29T05:16:41Z)
• An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games [16.95910326345246]
  We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function.<n>This problem arises for example in game-inspired machine learning applications, such as distributionally-robust learning.<n>We introduce and analyze a particle-based method that enjoys guaranteed local convergence for this problem.
  arXiv  Detail & Related papers  (2022-11-02T17:03:40Z)
• Explicit Tradeoffs between Adversarial and Natural Distributional Robustness [48.44639585732391]
  In practice, models need to enjoy both types of robustness to ensure reliability. In this work, we show that in fact, explicit tradeoffs exist between adversarial and natural distributional robustness.
  arXiv  Detail & Related papers  (2022-09-15T19:58:01Z)
• Bounded rationality for relaxing best response and mutual consistency: An information-theoretic model of partial self-reference [0.0]
  This work focuses on some of the assumptions underlying rationality such as mutual consistency and best-response. We consider ways to relax these assumptions using concepts from level-$k$ reasoning and quantal response equilibrium (QRE) respectively.
  arXiv  Detail & Related papers  (2021-06-30T06:56:56Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)
• Calibration of Shared Equilibria in General Sum Partially Observable Markov Games [15.572157454411533]
  We consider a general sum partially observable Markov game where agents of different types share a single policy network. This paper aims at i) formally understanding equilibria reached by such agents, and ii) matching emergent phenomena of such equilibria to real-world targets.
  arXiv  Detail & Related papers  (2020-06-23T15:14:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.