arXiv:2511.14220v3 Announce Type: replace Abstract: Model-based reinforcement learning (RL) methods that leverage search are responsible for many milestone breakthroughs in RL. Sequential Monte Carlo (SMC) recently emerged as an alternative to the Monte Carlo Tree Search (MCTS) algorithm which drove these breakthroughs. SMC is easier to parallelize and more suitable to GPU acceleration. However, it also suffers from large variance and path degeneracy which prevent it from scaling well with increased search depth, i.e., increased sequential compute. To address these problems, we introduce Twice
The paper addresses current limitations in model-based reinforcement learning with Sequential Monte Carlo (SMC), which has seen recent emergence as an alternative to MCTS, by proposing a new method to improve scalability.
Improving the scalability and efficiency of search algorithms in reinforcement learning is critical for advancing AI capabilities, particularly in complex decision-making and agentic systems.
The introduction of 'Twice Sequential Monte Carlo' offers a pathway to overcome current scaling barriers in SMC, potentially enabling more robust and deeper searches in AI applications.
- · AI researchers
- · Reinforcement learning applications
- · GPU manufacturers
- · AI agent developers
- · Less efficient search algorithms
More sophisticated and capable AI models can be developed due to improved search algorithms.
This could accelerate the deployment of autonomous AI agents in various industries by enhancing their decision-making capabilities.
The development of highly scalable search algorithms might contribute to the broader availability of computationally intensive AI, raising new compute demands.
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Read at arXiv cs.LG