Multi-Task Pre-training is a learning paradigm where a model is pre-trained simultaneously on a mixture of different objectives or datasets — rather than just one task (like MLM), the model optimizes a weighted sum of losses from multiple tasks (e.g., MLM + NSP + Translation + Summarization) to learn a more general representation.

Examples

• T5: Trained on a "mixture" of unsupervised denoising, translation, summarization, and classification tasks.
• MT-DNN: Multi-Task Deep Neural Network — combines GLUE tasks during pre-training.
• UniLM: Trained on simultaneous bidirectional, unidirectional, and seq2seq objectives.

Why It Matters

• Generalization: Prevents overfitting to the idiosyncrasies of a single objective.
• Transfer: Models pre-trained on many tasks transfer better to new, unseen tasks (Meta-learning).
• Efficiency: A single model can handle ANY task without task-specific architectural changes.

Multi-Task Pre-training is cross-training for AI — practicing many different skills simultaneously to build a robust, general-purpose model.