Unverified50% confidenceFactExact time
The goal of one-shot learning is to enable a model to understand and reproduce a task from just a single demonstration or example.
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9/1/2026
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Unverified一次模仿学习（One-Shot Imitation Learning）由Duan等人在2017年提出，让模型在元训练阶段观察大量演示-执行对以从单次演示中提取任务规格80% similarVerifiedFew-Shot Learning refers to providing a small number of examples (typically 2–8) in the prompt, allowing the model to complete new tasks through analogical reasoning.77% similarUnverifiedWhen a Skill is presented in the form of a few-shot example set, it essentially leverages the model's In-Context Learning capability without modifying the model weights.73% similarUnverifiedSelf-supervised learning is the core paradigm of the pre-training phase, where the model constructs training signals from the data itself through a next-token prediction task.73% similarUnverifiedOne-shot learning is not the same as zero-shot learning; the single demonstration relies on prior knowledge accumulated by the model from massive pretraining data.72% similar
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