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Search-Tree Scaling in Parallel Monte Carlo Tree Search

NeurIPS 2026 · Learning theory and algorithms › Combinatorial and search optimization · approach: Monte Carlo tree search

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Studies how the exploration-exploitation tradeoff in parallel Monte Carlo Tree Search (MCTS) interacts with the pUCT exploration coefficient and the degree of parallelism when combined with neural network evaluations.

Problem

In MCTS, exploration is set explicitly by the pUCT coefficient and implicitly by parallelism with neural evaluations, but the exploration coefficient is usually tuned as a hyperparameter while parallelism is often treated as a fixed systems choice.

Approach

Treats parallelism as a second, implicit exploration knob alongside the pUCT coefficient and studies their joint effect (search-tree scaling); the abstract is truncated so the specific method is not fully stated.

Limitations

• Only the abstract was available; full methods and results are unknown.

Experimental setup

Builds on: pUCT

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