Learning Where to Search: Subtree Exploration in Tree-Search

In planning with tree search, how search budget is allocated across the tree determines the final decision. Standard Monte Carlo Tree Search (MCTS) allocates this computation implicitly through repeated local tree-traversal decisions that do not globally prioritize reaching the most informative region of the tree to explore next. We introduce Learning to Allocate Search Resources (LASER), a framework that separates *where* to search from *how* to search. Our method uses a graph neural network to explicitly select a node in the current search tree from which to further explore by a bounded local MCTS. We formulate this process as a metalevel decision problem and train the node selector from action-level supervision, without requiring annotations of where search should occur. On Sokoban and sliding puzzles, LASER consistently outperforms standard MCTS and alternative subtree-selection strategies under matched search budgets. We further integrate our approach into EfficientZero by training the selector concurrently with a world model, value function, and policy during reinforcement learning, and observe improved aggregate performance on Atari 100k over baseline search methods.