Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS

• URL: http://arxiv.org/abs/2507.05557v1
• Date: Tue, 08 Jul 2025 00:41:12 GMT
• Title: Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS
• Authors: Alex ZH Dou, Zhongwei Wan, Dongfei Cui, Xin Wang, Jing Xiong, Haokun Lin, Chaofan Tao, Shen Yan, Mi Zhang,
• Abstract summary: We introduce R2-LLMs, a novel and versatile hierarchical retrieval-augmented reasoning framework.<n>R2-LLMs enhances inference-time generalization by integrating dual-level retrieval-based in-context learning.<n> Empirical evaluations on the MATH500, GSM8K, and OlympiadBench-TO datasets achieve substantial relative improvement.
• Score: 19.394761422323853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time scaling has emerged as a promising paradigm in language modeling, leveraging additional computational resources at inference time to enhance model performance. In this work, we introduce R2-LLMs, a novel and versatile hierarchical retrieval-augmented reasoning framework designed to improve test-time scaling in large language models (LLMs) without requiring distillation from more advanced models to obtain chain-of-thought (CoT) training data. R2-LLMs enhances inference-time generalization by integrating dual-level retrieval-based in-context learning: (1) At the coarse level, our approach extracts abstract templates from complex reasoning problems and retrieves similar problem-answer pairs to facilitate high-level in-context learning; (2) At the fine level, during Monte Carlo Tree Search (MCTS), R2-LLMs efficiently retrieves analogous intermediate solution steps from reference mathematical problem datasets, refining step-wise reasoning with the aid of a process reward model (PRM) for scoring. R2-LLMs is a robust hierarchical reasoning-augmentation method that enhances in-context-level reasoning while seamlessly integrating with step-level tree search methods. Utilizing PRM, it refines both candidate generation and decision-making for improved reasoning accuracy. Empirical evaluations on the MATH500, GSM8K, and OlympiadBench-TO datasets achieve substantial relative improvement with an increase of up to 16% using LLaMA-3.1-8B compared to the baselines, showcasing the effectiveness of our approach in complex reasoning tasks.

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