Pre-Trained Language Models for Interactive Decision-Making

Understand

Language model (LM) pre-training is useful in many language processing tasks.

• But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an approach for using LMs to scaffold learning and generalization in general sequential decision-making problems.
• In this approach, goals and observations are represented as a sequence of embeddings, and a policy network initialized with a pre-trained LM predicts the next action.
• We demonstrate that this framework enables effective combinatorial generalization across different environments and supervisory modalities.