DeepSeek manages overfitting during fine-tuning by combining established regularization techniques with careful data and training process design. Overfitting occurs when a model becomes too specialized to the training data, losing its ability to generalize to new inputs. To prevent this, DeepSeek employs methods like dropout, weight decay, and data augmentation. For example, dropout layers are added to neural networks to randomly disable a percentage of neurons during training, forcing the model to rely on diverse patterns rather than memorizing specific examples. Weight decay (L2 regularization) is applied to penalize large parameter values, encouraging simpler models that are less likely to overfit.
Another key strategy involves adjusting the training process itself. DeepSeek uses early stopping, where training is halted once validation performance plateaus or starts declining, preventing the model from over-optimizing on the training set. Additionally, the framework often applies progressive fine-tuning—starting with a lower learning rate for the pre-trained base model while using a slightly higher rate for task-specific layers. This balances retaining general knowledge from pre-training with adapting to new data. For instance, when fine-tuning a language model for a domain like legal text, the base layers might update slowly to preserve grammatical understanding, while the top layers adjust more quickly to learn legal terminology.
Data handling also plays a critical role. DeepSeek ensures diverse and representative training data, often augmenting datasets with techniques like synonym replacement or paraphrasing for text tasks. For structured data, noise injection or feature shuffling might be used. The framework also employs cross-validation, splitting data into multiple training/validation subsets to verify consistent performance across different samples. If a model shows significant performance gaps between training and validation sets—like 95% training accuracy but 70% validation accuracy—DeepSeek’s pipelines automatically trigger hyperparameter adjustments or additional regularization to address the imbalance before final deployment.