3 papers
cs.AI2026
Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT
Cen Lu, Yung-Chen Tang, Andrea Cavallaro
Developers judge a model checkpoint by how it behaves. After supervised fine-tuning (SFT), two checkpoints that perform about the same across relevant benchmarks are treated as int…
cs.LG2025
CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
Yung-Chen Tang, Pin-Yu Chen, Andrea Cavallaro
Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks. However, popular methods like Best-of-…
cs.CR2025
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
Lei Hsiung, Tianyu Pang, Yung-Chen Tang +4
Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. Howe…