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

Abstract:Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequential and deterministic nature, principled runtime-scaling of MCTS with parallel compute remains a major challenge. We introduce Particle MCTS (PMCTS), a principled parallel MCTS algorithm suited for neural network evaluations and designed for GPU-acceleration with batch-parallelization. We establish policy improvement guarentees for modern MCTS algorithms and show that PMCTS maintains them. Empirically, PMCTS scales well with parallel compute and consistently outperforms or compares well to the popular heuristic-based baselines across a range of MCTS and RL evaluation domains, including the board games chess and Go and popular discrete action and continuous control benchmarks.

Machine Learning (cs.LG)

arXiv:2605.08982 [cs.LG]

(or arXiv:2605.08982v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.08982 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.