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cs.CL2026
LP-SFT: Keeping Plausible Alternatives Alive in Supervised Fine-Tuning
Yueyang Wang, Baolong Bi, Shuo Lu +3
Supervised fine-tuning (SFT) is widely used to adapt pretrained language models to downstream domains, but can over-specialize the model and degrade its pre-existing capabilities.…
cs.CL2026
EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
Deyao Zhu, Xin Zhou, Shengling Qin +44
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less unders…