Verified65% confidenceFactExact time
Mid-training refers to the stage inserted between pre-training and post-training in which the model continues to be trained on large-scale domain-relevant data, and its difference from supervised fine-tuning lies in the larger data scale and the goal being more oriented toward capability shaping rather than task alignment.
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10/31/2026
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UnverifiedTraining pipeline refers to the complete sequence of steps from data preparation to pre-training, mid-training and post-training; disclosing a training pipeline does not mean the model has finished training or that weights will be made public.77% similarVerifiedSelf-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.76% similarVerifiedA pre-trained model refers to a model that has been trained in advance on large-scale datasets, which users can directly fine-tune or call to complete specific tasks.76% similarUnverifiedA recent study compared embedded training and staged training as two data-driven closed-loop modeling methods.76% similarUnverifiedThe pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.76% similar
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