Conditioned Language Policy: A General Framework for Steerable
Multi-Objective Finetuning
Conditioned Language Policy: A General Framework for Steerable
Multi-Objective Finetuning
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge here is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and efficient manner. This paper presents Conditioned Language Policy (CLP), a general framework for finetuning language models …