Unverified50% confidenceOpinionExact time
Fine-tuning capability requires the support of dataset preparation and training practice; simply reading theory makes it hard to develop an engineering feel.
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Long-term
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10/30/2026
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UnverifiedThe training of deep learning models is not a fully interpretable process, and researchers often discover capabilities that were not explicitly designed during post-training evaluations, such as in-context learning, chain-of-thought reasoning, tool-calling tendencies, and so on.79% similarUnverified训练秘诀（Training Recipe）包括数据配比策略、学习率调度曲线、过滤规则、对齐技术等，往往比模型架构本身更难被复现78% similarVerifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.78% similarUnverifiedPrompt-engineering-based methods rely on carefully designed prompts to guide reasoning but lack persistent learning capabilities, while reinforcement-learning-based methods require large amounts of samples and computational resources.77% similarUnverifiedRestricting training data or fine-tuning directions can only affect a model's default behavior, and cannot prevent technically capable individuals from bypassing restrictions through prompt engineering or model modification.77% similar
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