Unverified50% confidenceFactExact time
The core difference between reasoning models and ordinary LLMs lies in the training approach rather than the architecture; reasoning models introduce a reinforcement learning stage on top of pretraining and supervised fine-tuning.
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9/20/2026
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VerifiedTraining strategies for mathematical LLMs include continued pre-training on large-scale mathematical corpora on top of general pre-training, followed by supervised fine-tuning with chain-of-thought reasoning data, combined with reinforcement learning using process reward models.80% similarUnverifiedLLM采用无状态设计，模型权重在训练后被固化，推理时不会因某次对话而改变内部参数78% similarUnverified选用标准指令微调模型而非推理模型是CLI工具场景的正确工程决策，因为推理模型训练目标偏向正确性而非格式合规性76% similarUnverified在代码数据上训练的LLM不仅提升了编程能力，还显著增强了模型的逻辑推理能力，代码训练和逻辑推理能力之间存在正向迁移效应76% similar
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