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George Pappas

University of Pennsylvania

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#artificial intelligence Preprint Oct 2026

When to Follow the Teacher: Advantage-Based On-Policy Distillation

Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student. However, standard OPD trains the student to follow the teacher at every state of the trajectory, assuming that teacher guidance is always beneficial. We...

Mahdi Sabbaghi, George Pappas, H. Hassani et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Disagreement-Guided Weak-to-Strong Routing

Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable deployment challenging. Stronger models can provide assistance when needed, but deciding when to route a weak-model response to a stronger model ofte...

Sima Noorani, Shayan Kiyani, George Pappas et al. · 0 citations

Jailbreaking Jailbreaks: A Proactive Defense for LLMs

P RO A CT represents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective attacks.

Wei-Liang Zhao, Daniel Ben-Levi, Jinjun Peng et al. · 1 citation

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