今天Lau博士端上一盘硬菜：结合OpenAI、谷歌DeepMind发表的共7篇论文，一口气带你真正搞懂Scaling Law。
1. Kaplan, J., et al. (2020). Scaling Laws for Neural Language Models. arXiv:2001.08361
2. Hoffmann, J., et al. (2022). Training Compute-Optimal Large Language Models (Chinchilla). arXiv:2203.15556, NeurIPS 2022
3. Wei, J., et al. (2022). Emergent Abilities of Large Language Models. arXiv:2206.07682, TMLR
4. Schaeffer, R., et al. (2023). Are Emergent Abilities of Large Language Models a Mirage? arXiv:2304.15004, NeurIPS 2023 最佳论文
5. Muennighoff, N., et al. (2023). Scaling Data-Constrained Language Models. arXiv:2305.16264, NeurIPS 2023
6. Sardana, N., & Frankle, J. (2024). Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. arXiv:2401.00448, ICML 2024
7. Hestness, J., et al. (2017). Deep Learning Scaling is Predictable, Empirically. arXiv:1712.00409
8. OpenAI (2024). Learning to Reason with LLMs (o1 发布博客)
