PaLM

PaLM introduced Google’s Pathways era of training, where one model can be trained efficiently across many accelerators and generalize across tasks. The model is a high-capacity, dense Transformer trained on a broad mix of web, books, code, and multilingual text. It excels at few-shot learning, long-form answers, math and logic with chain-of-thought prompting, and code generation through PaLM-Coder variants. Engineering advances included large-batch optimization, memory-efficient sharding, and robust data filtering, which enabled stable training at unprecedented scale. In practice, PaLM served as the foundation for research and product lines that followed, influencing techniques for reasoning prompts, multilingual transfer, and code-focused fine-tunes, and setting the stage for successors like PaLM 2 and later Gemini models.