Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
arXiv:2602.03141v4 Announce Type: replace Abstract: While Large Reasoning Models (LRMs) have demonstrated impressive capabilities in solving complex tasks through the generation of long reasoning chains, this reliance on verbose generation results in significant latency and computational overhead. To address these challenges, we propose \textbf{CoSMo} (\textbf{Co}nsistency-Guided \textbf{S}plit-\textbf{M}erge \textbf{O}ptimization), a framework designed to eliminate structural redundancy rather than indiscriminately restricting token volume. Specifically, CoSMo utilizes a split-merge algorithm
The increasing complexity and computational cost of Large Reasoning Models are driving research into methods that can maintain advanced capabilities while reducing resource intensity.
Optimizing reasoning efficiency in AI models is crucial for scaling their deployment and making them more economically viable for a wider range of applications, democratizing access to advanced AI.
New methodologies are emerging that move beyond simply restricting token volume, focusing instead on structural redundancy to enhance efficiency without sacrificing the depth of AI reasoning.
- · AI developers
- · Cloud providers
- · SaaS companies leveraging AI
- · Developers of edge AI hardware
- · Companies with highly inefficient AI models
- · Users relying solely on brute-force computational power for AI
More cost-effective deployment of complex AI models becomes feasible, particularly for intricate tasks requiring extensive reasoning.
This efficiency gain could accelerate the adoption of advanced AI in budget-sensitive or latency-critical applications.
Reduced compute demands for sophisticated AI could lower the barrier to entry, fostering innovation and decentralization in AI development, potentially impacting the competitive landscape of large AI labs.
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