Hot-Start Chinese Language Modeling:Visual Glyphs Accelerate Sample-Efficient Learning 事件

Hot-Start Chinese Language Modeling:Visual Glyphs Accelerate Sample-Efficient Learning arXiv:2601.09566v4 Announce Type: replace Abstract: In this work, we study whether rendering Chinese characters as visual glyph images, rather than discrete token IDs as mainstream LLMs do, providing an inductive bias for character-level language modeling. Our central finding gives a double-edged insight: visual inputs produce a pronounced hot-start effect, more than doubling early-stage accuracy within the f