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cs.CL2026
Smooth Scaling Laws Hide Stepwise Token Learning
Pingjie Wang, Zechen Hu, Peiru Yang +2
Language model loss follows remarkably regular scaling laws over model and data size, yet it remains unclear why the aggregate loss should exhibit a power-law form. Existing explan…
cs.LG2026
JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation
Yebin Yang, Huaijin Wu, Fu Guo +5
LLMs have traditionally scaled along dense dimensions, where performance is coupled with near-linear increases in computational cost. While MoE decouples capacity from compute, it…