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Neural Thermodynamic Laws for Large Language Model Training
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Beyond neural scaling laws, little is known about the laws underlying large language models (LLMs). We introduce Neural Thermodynamic Laws (NTL) -- a new framework that offers fresh insights into LLM training dynamics. On the theoretical side, we demonstrate that key thermodynamic quantities (e.g., temperature, entropy, heat capacity, thermal conduction) and classical thermodynamic principles (e.g., the three laws of thermodynamics and the equipartition theorem) naturally emerge under river-valley loss landscape assumptions. On the practical side, this scientific perspective yields intuitive guidelines for designing learning rate schedules.
Forward citations
Cited by 2 Pith papers
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Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Falcon-H1 reports competitive benchmark scores for a 0.5B to 34B family of parallel hybrid attention/Mamba-2 models, claiming 2x to 4x parameter efficiency versus dense transformers.
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Mpemba Effect in Large-Language Model Training Dynamics: A Minimal Analysis of the Valley-River model
The authors derive conditions under which a higher plateau learning rate accelerates LLM training convergence via the Mpemba effect, but the central derivative formula is incorrect.
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