CORTEXA
← Browse

Yueyang Wang

2 papers indexed

arxivcs.CLcs.LG2026-07-06

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Deyao Zhu, Xin Zhou, Shengling Qin, Xuekai Zhu, Hangliang Ding, Shu Zhong, et al.

Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less understood. Analyzing roughly 38,000 hours of agent interaction with the environment across 134 real world…

View free PDFSource page
arxivcs.CLcs.LG2026-07-06

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

Yueyang Wang, Baolong Bi, Shuo Lu, Jingyuan Zhang, Jiajun Shi

Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities. Standard cross-entropy fine-tuning promotes only the observed labe…

View free PDFSource page