Author

C. Wu

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#artificial intelligence Preprint Oct 2026

Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning

A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data often causes poor generalization and catastrophic forgetting. As such, the conventional wisdom is that on-policy training is a prerequisite for continual learning. In prac...

C. Wu, Thomas T. Zhang, Aditi Raghunathan · 0 citations

#artificial intelligence Nov 2025

Jailbreaking in the Haystack

NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by appending benign, model-generated content to harmful user goals to reveal fundamental vulnerabilities in modern LMs.

R. Shah, C. Wu, Shashwat Saxena et al. · arXiv.org · 4 citations