The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.
— PaLM 2 Technical Report, PDF
Recent articles
- Distributing Go binaries like sqlite-scanner through PyPI using go-to-wheel - 4th February 2026
- Moltbook is the most interesting place on the internet right now - 30th January 2026
- Adding dynamic features to an aggressively cached website - 28th January 2026