# Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions

arXiv: https://arxiv.org/abs/2210.12628 · HTML: https://mlsanity.com/paper/2210.12628/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 23 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Atari Games with Limited Data](https://mlsanity.com/paper/2111.00210/) (2021)
- [Monte-Carlo Tree Search as Regularized Policy Optimization](https://mlsanity.com/paper/2007.12509/) (2020)
- [Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search](https://mlsanity.com/paper/2012.07910/) (2020)

## Built on by (most cited first, 4 of 4)

- [Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction](https://mlsanity.com/paper/2310.06513/) (2023)
- [Data-Efficient Multi-Agent Spatial Planning with LLMs](https://mlsanity.com/paper/2502.18822/) (2025)
- [Scaling Tasks, Not Samples: Mastering Humanoid Control through Multi-Task Model-Based Reinforcement Learning](https://mlsanity.com/paper/2603.01452/) (2026)
- [Discovering Lattice Reduction Strategies via Self-Play](https://mlsanity.com/paper/2606.15301/) (2026)

## Abstract (arXiv)

One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various challenging domains. However, the expensive time cost during search severely restricts its scope for applications. This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively. We give theoretical bounds of the proposed method and evaluate the performance and computations on 9 × 9 Go board games and Atari games. Experiments show that our method can achieve comparable performances to the original search algorithm while requiring less than 50% search time on average. We believe that this approach is a viable alternative for tasks under limited time and resources. The code is available at urlhttps://github.com/YeWR/V-MCTS.git.