百萬成本揭祕LLM訓練黃金法則，階躍星辰推出全領域適用的超參數優化工具
在人工智能的激烈競爭中，一場耗資百萬美元的大規模實驗正悄然改變着大語言模型的訓練方式。階躍星辰研究團隊日前發佈重磅研究成果，他們通過耗費近100萬NVIDIA H800GPU小時的算力，從零開始訓練了3，700個不同規模的模型，累計訓練了驚人的100萬億個token，揭示出一條被稱爲"Step Law"的普適性縮放規律，爲大語言模型的高效訓練提供了全新指南。這項研究不僅僅是對超參數優化的探索，更是第一個全面考察模型最優超參在不同形狀、稀疏度和數據分佈下穩定性的工作。研究結果表明，無
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