On-policy distillation (OPD) trains agents on their own trajectories using supervision from a stronger teacher. We uncover a counterintuitive phenomenon in multi-turn agentic tasks: a smaller, initially weaker student can substantially outperform its teacher during training, only to regress as distillation continues. W...
Ji-Ke Zhong, Ming Li, Yu-Xiang Lai et al.· 0 citations
Large audio-language models (LALMs) expand language models to process and interpret audio, but also expose them to heterogeneous audio jailbreaks. We ask whether successful jailbreaks reflect failures to recognize harmful intent or failures occurring after such recognition. Layer-wise probing reveals the latter: risk-r...
Yu-Ling Liao, Tzu-Chin Chiu, Zong-You Chen et al.· 1 citation
A three-stage evaluation pipeline is designed that progressively reduces textual guidance to systematically evaluate LALMs' in-context learning ability in the audio modality, uncovering a consistent asymmetry across LALMs.
Yen-Ting Piao, Jay Chiehen Liao, W. Chien et al.· 1 citation
The results suggest context learning hinges on not only content acquisition but also specification acquisition, and designs a deliberately simple intervention PSCI (private specification-contract induction) which extracts local specifications and enforces them through adversarial checking and repair.
Jike Zhong, Ming Li, Yuxiang Lai et al.· 1 citation
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