arXiv:2606.17687v1 Announce Type: new Abstract: Despite remarkable performance on complex tasks, Large Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries. Existing efforts to mitigate this inefficiency typically rely on discrete reasoning modes or fixed budget tiers, lacking a principled criterion of when reasoning is sufficient. In this work, we introduce Minimal Sufficient CoT (MSC), defined as the shortest prefix of a CoT trajectory which is adequate for producing the correct answer. We empirically show that
The rapid development and deployment of large reasoning models necessitate continuous innovation in efficiency to manage escalating computational demands, making this research timely.
Improving the efficiency of Large Reasoning Models by reducing computational overhead for common tasks directly impacts the scalability and cost-effectiveness of AI applications, especially AI agents.
The proposal of Minimal Sufficient CoT (MSC) offers a principled method to dynamically determine reasoning sufficiency, potentially leading to more adaptive and resource-optimized AI systems.
- · AI developers
- · Cloud computing providers
- · Edge AI manufacturers
- · Enterprise AI adopters
- · Inefficient AI models
- · Companies with high compute costs
- · Fixed-budget reasoning approaches
Reduced operational costs and faster inference times for AI applications.
Increased accessibility and broader adoption of complex AI in resource-constrained environments.
Acceleration in the development and deployment of sophisticated AI agents that can adapt their reasoning on the fly.
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Read at arXiv cs.CL