Game-playing neural networks like AlphaZero achieve superhuman performance in board games by augmenting the raw policy with a test-time search harness and distilling the stronger, augmented policy back into the network. Why aren’t similar techniques used in language modelling today? The DeepSeek-R1 authors mention they found limited success with MCTS; Finbarr Timbers has an excellent post on why they may have faced this problem, namely their choice of UCT instead of pUCT.
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