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PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling

Yaniv Oren, Viliam Vadocz, Joery A. De Vries, Wendelin Böhmer, Matthijs T. J. Spaan, and Hendrik Baier. PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling. arXiv:2605.08982, 2026.

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Abstract

Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement through search with increasing popularity for real world applications. Due to the sequential and deterministic nature of its search, runtime-scaling of MCTS with parallel compute remains a major challenge. We introduce Particle MCTS (PMCTS), to our knowledge the first principled parallel MCTS algorithm which is suited for neural network evaluations and can preserve formal policy improvement guarantees. Empirically, PMCTS scales well with parallel compute and significantly outperforms the popular heuristic-based baselines across domains.

BibTeX Entry

@Misc{Oren26pmcts,
  author =       {Oren, Yaniv and Vadocz, Viliam and De Vries, Joery
                  A. and Wendelin B{\"o}hmer and Spaan, Matthijs
                  T. J. and Baier, Hendrik},
  title =        {{PMCTS}: Particle {M}onte {C}arlo Tree Search for
                  Principled Parallelized Inference Time Scaling},
  howpublished = {arXiv:2605.08982},
  year =         2026
}

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