Implicit Strategic Optimization: Rethinking Long-Horizon Decision-Making in Adversarial Poker Environments

• URL: http://arxiv.org/abs/2602.08041v1
• Date: Sun, 08 Feb 2026 16:17:46 GMT
• Title: Implicit Strategic Optimization: Rethinking Long-Horizon Decision-Making in Adversarial Poker Environments
• Authors: Boyang Xia, Weiyou Tian, Qingnan Ren, Jiaqi Huang, Jie Xiao, Shuo Lu, Kai Wang, Lynn Ai, Eric Yang, Bill Shi,
• Abstract summary: Implicit Strategic Optimization is a prediction-aware framework for training large language model (LLM) agents for adversarial games.<n>We prove sublinear contextual regret and equilibrium convergence guarantees whose dominant terms scale with the number of context mispredictions.<n>Experiments in 6-player No-Limit Texas Hold'em and competitive Pokemon show consistent improvements in long-term return.
• Score: 9.732494293258828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training large language model (LLM) agents for adversarial games is often driven by episodic objectives such as win rate. In long-horizon settings, however, payoffs are shaped by latent strategic externalities that evolve over time, so myopic optimization and variation-based regret analyses can become vacuous even when the dynamics are predictable. To solve this problem, we introduce Implicit Strategic Optimization (ISO), a prediction-aware framework in which each agent forecasts the current strategic context and uses it to update its policy online. ISO combines a Strategic Reward Model (SRM) that estimates the long-run strategic value of actions with iso-grpo, a context-conditioned optimistic learning rule. We prove sublinear contextual regret and equilibrium convergence guarantees whose dominant terms scale with the number of context mispredictions; when prediction errors are bounded, our bounds recover the static-game rates obtained when strategic externalities are known. Experiments in 6-player No-Limit Texas Hold'em and competitive Pokemon show consistent improvements in long-term return over strong LLM and RL baselines, and graceful degradation under controlled prediction noise.

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