DeepSeek V4 Pro improves on its earlier preview without changing the underlying architecture. Its post-training process creates separate specialist checkpointsPost-training adapts a pretrained AI model to improve its behavior, capabilities, or alignment after the main training stage. for mathematics, coding and agentic work, then distills the abilities of more than ten teachers into one student modelKnowledge distillation trains a smaller or different AI model to reproduce useful behavior learned from a stronger teacher model..
DeepSeek V4 Pro also drafts several tokens ahead instead of predicting only one token at a timeMulti-token prediction trains or runs a language model to predict several future tokens instead of only the immediate next token.. The reported result is substantially faster generation, showing how a recently published research technique can move quickly into a working model release.
DeepSeek V4 Pro keeps MIT-licensed open weightsAn open-weight AI model makes its learned parameter values available for others to download, inspect or run under a stated license., allowing independent hosts to compete on price even when the model is too large for most people to run at home. That availability gives users more control over hosting and model access than a closed service can provide.
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