self-training

Self-training uses a model's own predictions on unlabeled data as training labels for semi-supervised learning. **Process**: Train on labeled data → predict on unlabeled data → select high-confidence predictions → add as pseudo-labels → retrain on expanded dataset → iterate. **Why it works**: Model extracts patterns from unlabeled data structure, confident predictions often correct, bootstraps from small labeled set. **Selection strategies**: Confidence threshold, top-k predictions, curriculum (easy to hard), uncertainty sampling. **Risks**: Error propagation (wrong pseudo-labels reinforce errors), confirmation bias, domain shift between labeled/unlabeled. **Mitigation**: High confidence thresholds, noise-robust training, consistency regularization, multiple models. **For NLP**: Text classification, NER, sequence labeling, instruction tuning from raw text. **Related methods**: Co-training (multiple views), tri-training (multiple models), Mean Teacher. **Noisy Student**: Google's large-scale self-training for vision - student trained on noisy augmented pseudo-labeled data. **Modern use**: Distillation from large models, domain adaptation, low-resource scenarios. Foundational semi-supervised technique.