capability-transfer

The paper introduces Active Taskless Distillation (ATD), a method that transfers capabilities from a teacher model to a student model using only single-word responses on task-unrelated prompts, probing the behavioral shadows of post-training.

This paper studies strong-to-weak capability transfer at test time, showing that stronger models can build inference-time harnesses that nearly double weaker models' performance without parameter updates.

This paper introduces ReAD, a reinforcement-guided capability distillation framework that optimizes token budgets by accounting for cross-capability transfer in large language models. It demonstrates improved downstream utility and reduced harmful spillover compared to existing baselines.