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Post-Training Leaves Behavioral Shadows on Unrelated Decisions

238 upvotes · 24 SEP 2026 · Ziyang Zhang, Yubin Jing, Yuanhao Zeng et al.

This paper discovers that language models can learn from unrelated text after being trained on a specific task, and introduces a method called Active Taskless Distillation (ATD) that allows this capability transfer to happen without using target-task examples or teacher parameters. Practitioners might care because it could lead to more efficient and effective language model training.

active learninglanguage modelscapability transfercontinual learningprompt engineering