Go's search space is larger than the number of atoms in the universe, making exhaustive search impossible. AlphaGo's core breakthrough was using neural networks to intelligently guide its search, evaluating only the most promising moves and making an intractable problem solvable.

AlphaGo's architecture mimicked human cognition by pairing a 'fast thinking' neural network for intuition with a 'slow thinking' search algorithm for explicit planning. This hybrid model, combining pattern recognition with calculation, proved more powerful for tackling complex problems than either approach alone.

In domains like coding and math where correctness is automatically verifiable, AI can move beyond imitating humans (RLHF). Using pure reinforcement learning, or "experiential learning," models learn via self-play and can discover novel, superhuman strategies similar to AlphaGo's Move 37.

MCTS acts like the Dagger (Dataset Aggregation) algorithm in robotics. For every state in a game, even one on a losing path, MCTS provides a 'better' action. This teaches the policy not just the optimal path, but also how to recover and get back to it from suboptimal states, creating a more robust agent.

Instead of training on the single best action from its search (a one-hot label), AlphaGo's policy network learns to imitate the entire probability distribution of moves from MCTS. This 'soft label' contains far more information, enabling a much more effective and sample-efficient form of knowledge distillation.

Monte Carlo Tree Search (MCTS) acts as a 'policy improvement operator.' After the search finds a better move distribution, the policy network is trained to directly predict this improved distribution. This distills the expensive search process into the network itself, making it stronger over time.

Karpathy criticizes standard reinforcement learning as a noisy and inefficient process. It assigns credit or blame to an entire sequence of actions based on a single outcome bit (success/failure). This is like "sucking supervision through a straw," as it fails to identify which specific steps in a successful trajectory were actually correct.

Humans stop analyzing a game when they intuit a winning or losing position. AlphaGo’s value function mimics this by predicting the eventual outcome from any board state. This allows the search to be drastically shortened, as it doesn't need to play out every possibility to the very end.

On-policy reinforcement learning, where a model learns from its own generated actions and their consequences, is analogous to how humans learn from direct experience and mistakes. This contrasts with off-policy methods like supervised fine-tuning (SFT), which resemble simply imitating others' successful paths.