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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.00510 |
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| _version_ | 1866918422963552256 |
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| author | Kim, Hongbeen Lee, Juhyun Lee, Sanghyeon Choi, Kwanghoon Huh, Jaehyuk |
| author_facet | Kim, Hongbeen Lee, Juhyun Lee, Sanghyeon Choi, Kwanghoon Huh, Jaehyuk |
| contents | Monte Carlo Tree Search (MCTS) is an effective test-time compute scaling (TTCS) method for improving the reasoning performance of large language models, but its highly variable execution time leads to severe long-tail latency in practice. Existing optimizations such as positive early exit, reduce latency in favorable cases but are less effective when search continues without meaningful progress. We introduce {\it negative early exit}, which prunes unproductive MCTS trajectories, and an {\it adaptive boosting mechanism} that reallocates reclaimed computation to reduce resource contention among concurrent searches. Integrated into vLLM, these techniques substantially reduce p99 end-to-end latency while improving throughput and maintaining reasoning accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling Kim, Hongbeen Lee, Juhyun Lee, Sanghyeon Choi, Kwanghoon Huh, Jaehyuk Artificial Intelligence Monte Carlo Tree Search (MCTS) is an effective test-time compute scaling (TTCS) method for improving the reasoning performance of large language models, but its highly variable execution time leads to severe long-tail latency in practice. Existing optimizations such as positive early exit, reduce latency in favorable cases but are less effective when search continues without meaningful progress. We introduce {\it negative early exit}, which prunes unproductive MCTS trajectories, and an {\it adaptive boosting mechanism} that reallocates reclaimed computation to reduce resource contention among concurrent searches. Integrated into vLLM, these techniques substantially reduce p99 end-to-end latency while improving throughput and maintaining reasoning accuracy. |
| title | Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.00510 |