Advances in AI training are primarily driven by algorithmic innovation, data availability, and the amount of compute used for training. The measure of compute used to train a single model, rather than the capacity of an entire datacenter or the speed of a single GPU, is a crucial factor correlating to the power of our best AI models. On the algorithmic side, Sophia, Second-order Clipped Stochastic Optimization, a simple scalable second-order optimizer, has been proposed. It uses a light-weight estimate of the diagonal Hessian as the pre-conditioner and achieves a 2x speed-up compared with Adam in the number of steps, total compute, and wall-clock time on language modeling with GPT-2 models.

Will the largest AI training run by compute announced in 2025 utilize Sophia, Second-order Clipped Stochastic Optimization in its training process?

Resolution Criteria:

This question will resolve positively if a credible source, such as a reputable AI research organization, AI company, or academic paper, confirms that the largest AI training run by compute announced in 2025 utilizes Sophia, Second-order Clipped Stochastic Optimization. The training run should be the one that uses the most amount of compute to train a single model announced in the year 2025.

The question will resolve negatively if it is confirmed by a credible source that the largest AI training run by compute announced in 2025 does not utilize Sophia, Second-order Clipped Stochastic Optimization, by the end of 2028.

If no information about the largest AI training run by compute announced in 2025 is available from credible sources by the end of 2028, the question will resolve as N/A.