Model Retraining
Model Retraining refers to the process of updating a machine learning model’s parameters using new data or modified objectives to improve performance, adapt to distribution shifts, or incorporate new capabilities. In the context of Large Language Models (LLMs), this often involves fine-tuning, Continual Learning, or full-scale pretraining updates.
Key Concepts
- Data Drift: Changes in the statistical properties of target variables over time, necessitating retraining to maintain accuracy.
- Catastrophic Forgetting: The tendency of neural networks to overwrite previously learned information when trained on new data.
- Compute Efficiency: Balancing the cost of retraining against the marginal gain in model performance.
Recent Developments & Related Techniques
While retraining focuses on the training phase, inference optimization techniques often complement retraining strategies by reducing the computational load of deployed models.
- Speculative Decoding: A technique to accelerate inference by using a smaller “draft” model to propose tokens, which are then verified by the larger target model.
- DeepSeek’s DSparK: A novel speculative decoding technique developed by DeepSeek and Peking University. It aims for lossless LLM inference acceleration, reportedly making LLMs up to 85% faster. See DeepSeek’s DSparK: Lossless LLM Inference Acceleration via Speculative Decoding for detailed analysis.