가치 기반 적응형 MCTS를 통한 LLM 추론 효율화
Value-Based Adaptive MCTS for Efficient LLM Reasoning
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초록
Large Language Models (LLMs) improve reasoning by scaling test-time computation using techniques like Chain-of-Thought prompting and Monte Carlo Tree Search (MCTS). While recent methods such as ReST-MCTS* leverage learned process rewards and per-step value estimates, they rely on fixed branching and iteration budgets, which can be inefficient across tasks of varying difficulty. This work proposes an adaptive tree search framework that dynamically adjusts branching factors and search iteration based on difficulty which is measured by the node value. This approach allocates resources more effectively to complex tasks, improving accuracy and reducing runtime without extra training. We further evaluate the method under various configurations, including different value thresholds and maximum iteration limits. Experiments on the MATH and GSM8K dataset show consistent improvements over fixed-budget baselines across these settings, highlighting the robustness and efficiency benefits of adaptive resource allocation in LLM reasoning.
Abstract
Large Language Models (LLMs) improve reasoning by scaling test-time computation using techniques like Chain-of-Thought prompting and Monte Carlo Tree Search (MCTS). While recent methods such as ReST-MCTS* leverage learned process rewards and per-step value estimates, they rely on fixed branching and iteration budgets, which can be inefficient across tasks of varying difficulty. This work proposes an adaptive tree search framework that dynamically adjusts branching factors and search iteration based on difficulty which is measured by the node value. This approach allocates resources more effectively to complex tasks, improving accuracy and reducing runtime without extra training. We further evaluate the method under various configurations, including different value thresholds and maximum iteration limits. Experiments on the MATH and GSM8K dataset show consistent improvements over fixed-budget baselines across these settings, highlighting the robustness and efficiency benefits of adaptive resource allocation in LLM reasoning.