Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS (2411.18478v2)

Abstract: In-context learning (ICL) enables 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 High-level Automated Reasoning 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.

• The paper presents HiAR-ICL, a novel approach that replaces example-based in-context learning with automated reasoning thought cards.
• It leverages Monte Carlo Tree Search to derive and optimize atomic cognitive actions, achieving 79.6% accuracy on the MATH benchmark.
• The research demonstrates reduced computational cost and enhanced problem-solving efficiency, making advanced reasoning accessible for smaller LLMs.

Insightful Overview of "Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS"

The paper "Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS" presents a novel approach to enhance the reasoning capabilities of LLMs. The authors introduce HiAR-ICL, a high-level automated reasoning paradigm that transcends conventional in-context learning methods characterized by reliance on example-based learning. This paradigm revolutionizes the application of Monte Carlo Tree Search (MCTS) to explore reasoning paths, integrating advanced cognitive patterns into LLMs for complex mathematical problem-solving.

Core Contributions and Methodology

The primary contribution of this research lies in reimagining the context for in-context learning (ICL) by shifting from exemplar-dependent learning to abstract reasoning patterns. The paper delineates how HiAR-ICL employs MCTS to autonomously derive reasoning patterns, materializing into what the authors term as "thought cards." These cards encapsulate sequences of cognitive actions aimed at forming rational step-wise problem-solving approaches without constant dependence on meticulously crafted examples.

Key steps outlined in the HiAR-ICL method are:

1. Definition of Atomic Reasoning Actions: The authors propose five fundamental cognitive actions to emulate human cognitive problem-solving strategies, foundational to the construction of reasoning patterns.
2. Construction of Thought Cards via MCTS: Utilizing standard MCTS processes, the methodology expands upon derived reasoning patterns, optimizing them for efficiency and effectiveness.
3. Selection of Reasoning Patterns: A dynamic cognitive complexity framework is introduced to match problems with suitable thought cards, ensuring the selected pattern aligns with the problem's complexity.
4. Problem Solving and Verification: The model performs reasoning under selected patterns, employing verification mechanisms such as process reward models and self-consistency checks to confirm solution accuracy.

Empirical Results

Experimental results underscore HiAR-ICL's effectiveness, achieving state-of-the-art accuracy on the MATH benchmark with an accuracy of 79.6% using Qwen2.5-7B-Instruct, surpassing renowned models like GPT-4o and Claude 3.5. These results not only validate the superiority of the proposed method over existing example-based ICL approaches but also emphasize its capability to maintain high reasoning accuracy while reducing computational complexity. The performance analysis notably showcases HiAR-ICL's robust application to models under 10 billion parameters, highlighting its potential in optimizing smaller LLMs for large-scale problem-solving without the proportional increase in computational costs seen in more prominent models.

Theoretical and Practical Implications

The implications of this research are manifold. Theoretically, it challenges the prevailing notion that LLMs are bound by their initial training contexts and demonstrations. By equipping models with high-level reasoning patterns, HiAR-ICL proposes a paradigmatic shift akin to endowing the models with an intrinsic problem-solving mindset, rather than merely mimicking human-crafted demonstrations. Practically, the reduction in human intervention demands through automated reasoning patterns positions HiAR-ICL as a significant step towards increased efficiency and accuracy in AI-driven processes.

Future Directions

Looking forward, the scalability of the HiAR-ICL framework invites exploration into broader domains beyond mathematical reasoning. Further research could explore refining cognitive complexity frameworks, potentially personalizing thought cards for various problem types. The exploration of more complex environments and even tighter integrations with existing machine learning frameworks is expected to propel advancements in AI autonomy and reliability.

In summary, the study presents a significant advancement in AI thought processes, reinforcing a model's ability to autonomously navigate complex problems. The convergence of high-level cognitive patterning and advanced tree search methods signifies an innovative stride in LLM in-context learning, charting a course for future research and applications in AI reasoning and decision-making.