Unverified50% confidenceFactExact time
Training 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.
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Long-term
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11/5/2026
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Related Claims
VerifiedSelf-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.79% similarUnverifiedTreating execution trajectories as training data essentially extends the supervision signal from 'outcome supervision' to 'process supervision'.79% 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.77% similarUnverifiedMid-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.77% similarVerifiedTraining refers to repeatedly iterating model parameters with large-scale data, an offline, long-cycle task; inference is the forward computation process by which the model responds to each user request after going live, a real-time, highly concurrent online task.76% similar
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