SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning 事件

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