AI RESEARCH
Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
arXiv CS.LG
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ArXi:2605.08541v1 Announce Type: new Neural scaling laws approximate a language model's loss as a power-law function of parameter count $N$ and token count $D$. Following Chinchilla-style compute-optimal