The Gemini 2.5 family is the first to be trained on TPUv5p architecture[1]. Synchronous data-parallel training was employed to parallelise over multiple 8960-chip pods of Google’s TPUv5p accelerators, distributed across multiple datacenters[1].
Since the initial announcement of Gemini 1.5, significant advancements have been made in post-training methodologies, driven by a consistent focus on data quality across the Supervised Fine-Tuning (SFT), Reward Modeling (RM), and Reinforcement Learning (RL) stages[1]. A key focus has been leveraging the model itself to assist in these processes, enabling more efficient and nuanced quality control[1].
Get more accurate answers with Super Search, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
- Request a manual search from our human research team.