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cs.CL2026
Variable-Width Transformers
Zhaofeng Wu, Oliver Sieberling, Shawn Tan +3
Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across…
cs.CL2026
CodeAlchemy: Synthetic Code Rewriting at Scale
Ankit Gupta, Aditya Prasad, Rameswar Panda
Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats. While synthetic data has proven transformative for language models, code rem…