SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
arXiv:2603.08000v2 Announce Type: replace Abstract: Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasoning paths. However, the inherent verbosity of these processes frequently results in redundancy and overthinking. To address this issue, existing works leverage Group Relative Policy Optimization (GRPO) to reduce LRM output length, but their static length reward design cannot dynamically adapt according to the relative problem difficulty and response length distribution, causing over-compression and
The research addresses the growing computational cost and efficiency challenges of increasingly large language models, a current bottleneck in AI development.
Improving the efficiency of large reasoning models can significantly reduce operational costs and accelerate the deployment of more sophisticated AI applications across industries.
The proposed 'SmartThinker' method suggests a dynamic approach to optimizing reasoning path lengths, moving beyond static solutions and potentially enabling more adaptable and efficient AI systems.
- · AI developers
- · Cloud providers
- · Companies adopting large language models
- · AI research institutions
- · Inefficient AI architectures
- · Companies reliant on static CoT optimization
More cost-effective and scalable deployment of powerful large language models.
Accelerated development of complex AI agents and applications requiring sophisticated reasoning.
Enhanced competition in the AI market as smaller players gain access to more efficient reasoning capabilities.
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Read at arXiv cs.CL