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

2026-02-08

AI Summary

This work addresses the strategic myopia in long-horizon adversarial games, where conventional win-rate optimization methods fail due to neglecting time-evolving strategic externalities, leading to invalid regret analysis. To overcome this limitation, we propose the Implicit Strategy Optimization (ISO) framework, which uniquely couples strategic context prediction with policy optimization. ISO employs a strategic reward model and an optimism-based learning rule conditioned on contextual predictions to jointly model and optimize long-term strategic value in an online manner. Theoretically, we establish sublinear regret bounds dependent on contextual prediction error and provide convergence guarantees to equilibrium. Empirically, ISO significantly outperforms strong baselines—including large language models and reinforcement learning agents—in both six-player no-limit Texas Hold’em and Pokémon battles, while demonstrating robustness to prediction noise.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• long-horizon decision-making
• strategic externalities
• adversarial games
• episodic objectives
• contextual regret

Innovation

• Implicit Strategic Optimization
• Strategic Reward Model
• contextual regret
• long-horizon decision-making
• adversarial games