Google Demonstrates the Continued Relevance of Pre-Training

Google has updated its Gemini 2.5 Pro model family, releasing a technical report detailing their architecture and capabilities. The Gemini 2.5 models utilize a sparse mixture-of-experts (MoE) transformer, activating only select parameters for each task, which enhances efficiency and reduces computational costs. These models demonstrate improved large-scale training stability and performance due to advancements in pre-training techniques, which leverage additional compute and datasets. The training used fifth-generation TPUs with 8060-chip pods, a leap from the 4096-chip pods of the previous generation. Enhanced data quality methods and reinforcement learning during inference further bolster performance across tasks like math, coding, and reasoning. The Gemini 2.5 family, comprising the 2.5 Pro, 2.5 Flash, and 2.5 Flash-Lite models, is now fully available, with the 2.5 Pro recognized as one of the best-performing models, while the Flash variant offers the fastest output speed alongside competitive performance.

Source link