Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
Predictable scale: Part ii, farseer: A refined scaling law in large language models.arXiv preprint arXiv:2506.10972, 2025
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Position paper claims fixed exponents in scaling laws arise from generic mechanisms while coefficients vary with data and architecture, making the latter the focus for improvements.
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Scaling Laws for Neural-Network Quantum States
Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
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Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients
Position paper claims fixed exponents in scaling laws arise from generic mechanisms while coefficients vary with data and architecture, making the latter the focus for improvements.