Boosting Multimodal Reasoning with Automated Structured Thinking (2502.02339v3)

Abstract: Multimodal LLMs excel across diverse domains but struggle with complex visual reasoning tasks. Current approaches aim to incorporate structured thinking via two strategies: explicit search methods and post-training techniques. However, both approaches face significant limitations: Search-based methods suffer from computational inefficiency due to extensive solution space exploration, while post-training methods require substantial data, computational resources, and often encounter training instability. To address these limitations, we propose AStar, an \textbf{A}utomated \textbf{S}tructured \textbf{t}hinking paradigm for multimod\textbf{a}l \textbf{r}easoning. Our method introduces "thought cards", a lightweight library of high-level reasoning patterns abstracted from 500 prior samples using Monte Carlo Tree Search. For each test problem, AStar adaptively retrieves the optimal thought cards and seamlessly integrates these external explicit guidelines with the model's internal implicit reasoning capabilities. Extensive experiments demonstrate AStar's effectiveness and efficiency: using only 500 prior samples and a 7B backbone, our training-free framework achieves 53.9$\%$ accuracy on MathVerse (surpassing GPT-4o's 50.2%) and 32.7% on MathVision (versus GPT-4o's 30.4%). Further analysis reveals that AStar generalizes beyond multimodal reasoning to visual perception and understanding domains, and serves as a plug-and-play test-time inference method compatible with mainstream post-training techniques like GRPO.

• The paper introduces AStar, a novel framework that integrates MCTS with hierarchical cognitive patterns to boost multimodal reasoning.
• It achieves 54.0% on Math Verse using a 7B backbone and cuts inference overhead by 6.4x compared to traditional methods.
• AStar minimizes training data needs by reducing dependency on large datasets, demonstrating enhanced efficiency and scalability.

Overview of AStar for Multimodal Reasoning

This essay examines the development and evaluation of AStar, a novel framework introduced to enhance the reasoning capabilities of Multimodal LLMs (MLLMs). The research addresses the challenges of balancing performance and efficiency in complex visual reasoning tasks, specifically targeting the limitations of existing models that rely heavily on large datasets and extensive search spaces. AStar innovatively integrates Monte Carlo Tree Search (MCTS) with hierarchical cognitive patterns to achieve notable reasoning advancements.

Research Focus and Methodology

AStar is designed to automatically generate and leverage high-level reasoning patterns through MCTS-powered hierarchical structures, which facilitate cognitive reasoning from limited data resources. The methodology involves three primary stages: defining atomic visual reasoning actions, constructing thought cards via MCTS, and conducting adaptive reasoning with verification. Distinct from prevalent methods like explicit search and teacher-guided training, AStar focuses on efficient use of data and computational resources by minimizing the dependency on extensive pre-collected data during inference.

Numerical Results and Comparisons

The research demonstrates AStar's proficiency on established benchmarks, notably achieving 54.0% on Math Verse using a 7B backbone, outperforming GPT-4o's 50.2%. The framework's impressive structure enables substantial computational efficiency, evidenced by a 6.4x reduction in inference overhead compared to traditional tree search methods, and a 520x reduction in required training data compared to other training-focused approaches. These results confirm AStar's capability to challenge larger models like Intern VL2.5 and closed-source solutions such as GPT-4V, highlighting its superior data efficiency and comparable performance levels.

Theoretical and Practical Implications

Theoretically, AStar marks significant progress in enabling MLLMs to undertake structured reasoning without heavy reliance on extraneous data or computationally intensive processes. By integrating internal implicit reasoning with explicit higher-level guidelines, AStar paves the way for developing MLLMs with amplified cognitive abilities that are accessible and scalable. Practically, its efficient balancing of performance and resource allocation suggests potential applications in real-world scenarios where operational constraints and data availability present significant challenges.

Future Directions

The research opens several avenues for further exploration. The expansion and refinement of the reasoning action space could enhance the reasoning flexibility in more diverse contexts. Additionally, applying AStar in other domains could validate its adaptability and effectiveness beyond mathematical reasoning, potentially extending to areas like autonomous driving and other vision-intensive applications. Furthermore, integrating advanced verification models, even in resource-constrained environments, may provide additional performance gains.

Conclusion

AStar's success in enhancing MLLM performance with efficiency reaffirms the effectiveness of structured reasoning paradigms leveraging capabilities like MCTS. By addressing key limitations of existing frameworks, AStar provides a compelling approach for advancing multimodal reasoning research, encouraging developments that make strong AI both viable and more broadly applicable across diverse computational environments.