Researchers propose SPOKE method to improve AI poker strategy
A new arXiv paper introduces Strategic Preference Optimization with KL-anchored Equilibrium (SPOKE), a method designed to improve AI agents' strategic decision-making in poker. The researchers evaluated a static baseline and three frozen language models — SmallThinker, Qwen3, and Gemma-3 — finding their performance varied significantly between exact-game and six-player settings.
Key points
- SPOKE method reduces exploitability in Kuhn (0.213) and Leduc (1.081) poker
- Achieves 0.775 six-max top-1 agreement, highest among tested methods
- Maintains teacher KL divergence of 0.522, similar to SFT at 0.523
SPOKE combines teacher-relative value penalties, policy anchoring, risk constraints, and structured rationale supervision. Tested on a SmallThinker-4B-A0.6B policy across three runs, SPOKE achieved the lowest mean exploitability in Kuhn poker (0.213) and Leduc poker (1.081), and the highest six-max top-1 agreement (0.775), while maintaining teacher KL divergence comparable to supervised fine-tuning (0.522 vs 0.523). The authors say this provides preliminary evidence that strategic preference optimization can improve strategic quality while preserving teacher-consistent behavior.
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