# A Unified View of Large-scale Zero-sum Equilibrium Computation

arXiv: https://arxiv.org/abs/1411.5007 · HTML: https://mlsanity.com/paper/1411.5007/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2014.

## Built on by (most cited first, 7 of 7)

- [Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games](https://mlsanity.com/paper/2102.06973/) (2021)
- [Adapting to game trees in zero-sum imperfect information games](https://mlsanity.com/paper/2212.12567/) (2022)
- [Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent](https://mlsanity.com/paper/2110.04961/) (2021)
- [Local and adaptive mirror descents in extensive-form games](https://mlsanity.com/paper/2309.00656/) (2023)
- [Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games](https://mlsanity.com/paper/2208.09452/) (2022)
- [Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections](https://mlsanity.com/paper/2205.12031/) (2022)
- [Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation](https://mlsanity.com/paper/2509.13653/) (2025)

## Abstract (arXiv)

The task of computing approximate Nash equilibria in large zero-sum extensive-form games has received a tremendous amount of attention due mainly to the Annual Computer Poker Competition. Immediately after its inception, two competing and seemingly different approaches emerged---one an application of no-regret online learning, the other a sophisticated gradient method applied to a convex-concave saddle-point formulation. Since then, both approaches have grown in relative isolation with advancements on one side not effecting the other. In this paper, we rectify this by dissecting and, in a sense, unify the two views.