Transfer Learning
Transfer learning is a machine learning technique where knowledge acquired during training on one task is applied to improve performance on a different but related task. Rather than training a model from scratch, transfer learning leverages pre-trained models or learned feature representations as a starting point. This approach significantly reduces computational cost and data requirements, making it particularly valuable when the target task has limited training data available.
How Transfer Learning Works
In transfer learning, a model is first trained on a source task, typically using a large dataset. The learned weights, feature representations, or architectural patterns are then adapted for a target task. This can involve fine-tuning the entire model, freezing early layers while retraining later ones, or using the pre-trained features as input to a new classifier. The effectiveness depends on the similarity between source and target tasks—closer relationships generally yield better transfer.
Applications in AI Agents
Transfer learning is particularly relevant for AI agents that must operate across multiple domains or tasks. An agent trained on general language understanding can be adapted to specific control tasks, or visual recognition capabilities learned in one environment can accelerate learning in similar environments. This enables agents to bootstrap competence in new situations without requiring extensive retraining from random initialization.