The Thinking Boundary: Quantifying Reasoning Suitability of Multimodal Tasks via Dual Tuning
arXiv:2603.04415v1 Announce Type: new Abstract: While reasoning-enhanced Large Language Models (LLMs) have demonstrated remarkable advances in complex tasks such as mathematics and coding, their effectiveness across universal multimodal scenarios remains uncertain. The trend of releasing parallel "Instruct" and "Thinking" models by leading developers serves merely as a resource-intensive workaround, stemming from the lack of a criterion for determining when reasoning is truly beneficial. In this paper, we propose Dual Tuning, a framework designed to assess whether reasoning yields positive gains for target tasks under given base models and datasets. By jointly fine-tuning on paired Chain-of-Thought (CoT) and Direct-Answer (DA) data under controlled prompts, we systematically quantify and compare the gains of both training modes using the proposed metrics, and establish the "Thinking Boundary" to evaluate the suitability of reasoning training across diverse multimodal tasks, including
arXiv:2603.04415v1 Announce Type: new Abstract: While reasoning-enhanced Large Language Models (LLMs) have demonstrated remarkable advances in complex tasks such as mathematics and coding, their effectiveness across universal multimodal scenarios remains uncertain. The trend of releasing parallel "Instruct" and "Thinking" models by leading developers serves merely as a resource-intensive workaround, stemming from the lack of a criterion for determining when reasoning is truly beneficial. In this paper, we propose Dual Tuning, a framework designed to assess whether reasoning yields positive gains for target tasks under given base models and datasets. By jointly fine-tuning on paired Chain-of-Thought (CoT) and Direct-Answer (DA) data under controlled prompts, we systematically quantify and compare the gains of both training modes using the proposed metrics, and establish the "Thinking Boundary" to evaluate the suitability of reasoning training across diverse multimodal tasks, including spatial, mathematical, and multi-disciplinary domains. We further explore the impact of reinforcement training and thinking patterns on reasoning suitability, and validate whether the "Thinking Boundary" can guide data refinement. Our findings challenge the "reasoning-for-all" paradigm, providing practical guidance for identifying appropriate data and training strategies, and motivating the development of resource-efficient, adaptive auto-think systems.
Executive Summary
The article proposes a framework called Dual Tuning to assess the effectiveness of reasoning in multimodal tasks. It jointly fine-tunes Chain-of-Thought and Direct-Answer data to quantify the gains of reasoning and establishes the 'Thinking Boundary' to evaluate reasoning suitability across diverse tasks. The study challenges the 'reasoning-for-all' paradigm and provides guidance for identifying appropriate data and training strategies, motivating the development of resource-efficient auto-think systems.
Key Points
- ▸ Introduction of the Dual Tuning framework to assess reasoning effectiveness
- ▸ Establishment of the 'Thinking Boundary' to evaluate reasoning suitability
- ▸ Challenge to the 'reasoning-for-all' paradigm and guidance for data and training strategies
Merits
Novel Framework
The proposed Dual Tuning framework offers a systematic approach to evaluating the suitability of reasoning in multimodal tasks.
Comprehensive Evaluation
The study provides a thorough analysis of the impact of reinforcement training and thinking patterns on reasoning suitability.
Demerits
Limited Generalizability
The study's findings may not be generalizable to all types of multimodal tasks or datasets, potentially limiting the framework's applicability.
Expert Commentary
The proposed Dual Tuning framework represents a significant contribution to the field of multimodal AI, offering a systematic approach to evaluating the suitability of reasoning in diverse tasks. By establishing the 'Thinking Boundary', the study provides a valuable tool for guiding data and training strategies, and challenges the prevailing 'reasoning-for-all' paradigm. However, further research is needed to fully explore the framework's generalizability and potential applications.
Recommendations
- ✓ Future studies should investigate the applicability of the Dual Tuning framework to a broader range of multimodal tasks and datasets.
- ✓ Developers of AI systems should consider incorporating the 'Thinking Boundary' into their design and training processes to optimize reasoning effectiveness.