Skip to main content

Computer Science > Machine Learning

arXiv:2605.08982 (cs)
[Submitted on 9 May 2026 (v1), last revised 21 May 2026 (this version, v2)]

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

Authors:Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin Böhmer, Matthijs T. J. Spaan, Hendrik Baier
View PDF HTML (experimental)
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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.08982 [cs.LG]
  (or arXiv:2605.08982v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.08982

Submission history

From: Yaniv Oren [view email]
[v1] Sat, 9 May 2026 14:54:07 UTC (156 KB)
[v2] Thu, 21 May 2026 09:52:14 UTC (152 KB)
Full-text links:

Access Paper:

  • View PDF
  • HTML (experimental)
  • TeX Source
license icon view license

Current browse context:

cs.LG
< prev   |   next >
new | recent | 2026-05
Change to browse by:
cs

References & Citations

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

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.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)