PokerBench: Training Large Language Models to become Professional Poker Players

Abstract: We introduce PokerBench - a benchmark for evaluating the poker-playing abilities of LLMs. As LLMs excel in traditional NLP tasks, their application to complex, strategic games like poker poses a new challenge. Poker, an incomplete information game, demands a multitude of skills such as mathematics, reasoning, planning, strategy, and a deep understanding of game theory and human psychology. This makes Poker the ideal next frontier for LLMs. PokerBench consists of a comprehensive compilation of 11,000 most important scenarios, split between pre-flop and post-flop play, developed in collaboration with trained poker players. We evaluate prominent models including GPT-4, ChatGPT 3.5, and various Llama and Gemma series models, finding that all state-of-the-art LLMs underperform in playing optimal poker. However, after fine-tuning, these models show marked improvements. We validate PokerBench by having models with different scores compete with each other, demonstrating that higher scores on PokerBench lead to higher win rates in actual poker games. Through gameplay between our fine-tuned model and GPT-4, we also identify limitations of simple supervised fine-tuning for learning optimal playing strategy, suggesting the need for more advanced methodologies for effectively training LLMs to excel in games. PokerBench thus presents a unique benchmark for a quick and reliable evaluation of the poker-playing ability of LLMs as well as a comprehensive benchmark to study the progress of LLMs in complex game-playing scenarios.

• The paper presents PokerBench, a benchmark of 11,000 poker scenarios demonstrating that current LLMs, like GPT-4, initially achieve only around 53.55% accuracy in poker strategy.
• The paper shows that fine-tuning LLMs on the PokerBench dataset significantly improves performance, with some models reaching up to 78.26% accuracy.
• The paper validates the benchmark’s efficacy by using simulated competitions to correlate higher PokerBench scores with superior real-world poker performance.

An Analysis of PokerBench: Training LLMs to Become Professional Poker Players

The paper in discussion proposes PokerBench, a novel benchmark designed to evaluate the poker-playing abilities of LLMs. This work emerges from the growing interest in applying LLMs to tasks beyond traditional NLP, such as games that involve strategic decision-making and incomplete information, like poker.

Overview

Poker, particularly Texas No-Limit Hold'em, is identified as a challenging domain for LLMs due to its requirement of diverse skills, including mathematics, reasoning, strategy planning, and a nuanced understanding of human psychology. PokerBench claims to address the gap in evaluating LLMs' poker abilities by providing a benchmark composed of 11,000 poker scenarios, split between pre-flop (1,000 scenarios) and post-flop (10,000 scenarios) play. The benchmark was developed in collaboration with trained poker players, focusing on game theory optimal (GTO) strategies.

Key Findings

1. Underperformance of State-of-the-art LLMs: Initial evaluations of models such as GPT-4, ChatGPT 3.5, and Llama-3 series on PokerBench showed significant underperformance in playing optimal poker. GPT-4, despite being the best among them, demonstrated an accuracy of only 53.55%. This highlights that while modern LLMs excel in many domains, poker presents unique challenges.
2. Improvement through Fine-Tuning: The authors report significant improvements in poker-playing performance after fine-tuning models on the PokerBench training dataset. Notably, models like Llama-3-8B and Llama-2-7B achieved substantial gains, with post-fine-tuning accuracies reaching up to 78.26%.
3. Validation through Competition: To validate the reliability of PokerBench, fine-tuned models with varying scores competed against each other. It was demonstrated that models with higher benchmark scores achieved superior win rates in simulated poker games, thus confirming the benchmark's efficacy in predicting poker-playing prowess.
4. Comparison with GPT-4: Despite achieving a higher accuracy on PokerBench, the fine-tuned Llama model was outperformed by GPT-4 in direct competition. This discrepancy indicates potential limitations in the fine-tuning approach used and suggests that the suboptimal strategies chosen by models like GPT-4 can sometimes exploit weaknesses in the strategies learned by models trained specifically for optimal play.

Implications

The implications of this study are twofold:

• Practical Applications: Poker serves as an excellent testbed for LLMs' cognitive capabilities in strategy-based incomplete information settings. Success in such domains can pave the way for applications in areas requiring complex decision-making under uncertainty.
• Theoretical Insights: The significant improvements observed post-fine-tuning suggest that LLMs have the potential to learn and adapt complex game strategies theorecally. However, the trade-off between learning GTO strategies and exploiting non-optimal play in opponents reflects a nuanced challenge in AI development.

Future Directions

The authors suggest exploring advanced methodologies beyond simple supervised fine-tuning to train LLMs for optimal strategy learning in games, considering LLMs' current inability to naturally adopt or counter non-GTO strategies effectively. Other potential areas of research include developing interpretable model outputs to enable better understanding and customization of AI-driven strategies.

Conclusion

PokerBench presents a unique contribution to the field of artificial intelligence by providing a robust framework for assessing the poker-playing competence of LLMs. While promising results have been achieved, further work is necessary to realize the potential of LLMs in complex strategic settings and optimize their performance for real-world applications.