AGENTIC-SYSTEMSApril 3, 20261 min read18 views

Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling

Researchers introduce an adaptive parallel MCTS method to optimize test-time compute scaling for LLMs, significantly reducing p99 latency and improving throughput while maintaining reasoning accuracy.

Computer Science > Artificial Intelligence

Title:Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling

View PDF HTML (experimental)Abstract: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.

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Source: arXiv cs.AI Recent

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