PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers 事件

PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers arXiv:2605.30094v1 Announce Type: new Abstract: Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimization, requiring millions of core-hours of training. Large Language Models (LLMs) possess extensive poker knowledge but perform far below solver-based agents when asked to play directly. Traditional rule-based poker agents are in