Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

TL;DR

This paper introduces HiAR-ICL, a high-level automated reasoning paradigm using Monte Carlo Tree Search to improve in-context learning for complex reasoning tasks, surpassing traditional example-based methods.

Contribution

We propose a novel reasoning paradigm that shifts from example-based to pattern-based reasoning in ICL, utilizing MCTS to dynamically select reasoning strategies for better performance.

Findings

Achieves 80.6% accuracy on MATH with only 200 samples

Outperforms GPT-4o on math and AMC benchmarks

Generalizes across models and domains effectively

Abstract

In-context learning (ICL) enables large language models (LLMs) to perform downstream tasks through advanced prompting and high-quality demonstrations. However, traditional ICL paradigms encounter significant limitations in complex reasoning tasks, stemming primarily from their dependence on example quality and absence of explicit reasoning guidance. To address these challenges, we introduce HiAR-ICL, a **Hi**gh-level **A**utomated **R**easoning paradigm in **ICL** that shifts focus from specific examples to abstract reasoning patterns, thereby extending the conventional concept of "context" in ICL. Our approach begins by defining five atomic reasoning actions, upon which we employ Monte Carlo Tree Search to systematically construct high-level reasoning patterns. During inference, HiAR-ICL dynamically selects appropriate reasoning patterns based on problem attributes, providing explicit guidance for the model's reasoning process. Experiments demonstrate HiAR-ICL's effectiveness and efficiency: utilizing only 200 prior samples with Qwen2.5-7B-Instruct, our method achieves 80.6% accuracy on MATH and 62.5% on AMC, exceeding GPT-4o's 77.2% and 57.5%. Our approach enhances performance across models of varying sizes while generalizing effectively across domains. Further analysis reveals that HiAR-ICL can also serve as a plug-and-play inference method compatible with post-training techniques like GRPO. Code and data are available at https://github.com/jinyangwu/HiARICL.