My goal here is to introduce model based learning and show how language understanding merged with gameplay AI strategies recently. From early chess engines to modern language models (
OpenAI o1, 03, Google Gemini etc.). We examine key breakthroughs in game-playing AI—TD-Gammon, AlphaGo, and MuZero—and their contribution to current large language model architectures. Special focus on the convergence of Monte Carlo Tree Search (MCTS) with neural networks, and how these techniques transformed into today's chain-of-thought reasoning.

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