Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。 今天的主题是：Scaling Laws for PrecisionSummary This research paper investigates the impact of precision in training and inference on the performance of large language models. The authors explore how precision affects the effective parameter count and propose scaling laws that predict performance degradation due to low-precision training and post-training quantization. They find that overtrained models are more sensitive to post-training quantization, and that training larger models in lower precision might be computationally optimal. Their unified scaling law accounts for both training and post-training effects and predicts loss in varied precision settings, ultimately suggesting that the standard practice of training models in 16-bit might be suboptimal. 原文链接：https://arxiv.org/abs/2411.04330 解读链接：https://www.jiqizhixin.com/articles/2024-11-13-9