What happened
Researchers found a simple formula that predicts whether independently trained AI models will perform better when combined together: the gain depends on how different the models are from each other, following the relationship gain = 0.82 × divergence - 2.72. In practice, this means a team building AI systems can calculate upfront whether it's worth the compute cost to train separate specialist models and merge them, rather than guessing or burning resources on failed combinations.
Why it matters
For the first time, there's a measurable way to predict whether a collaborative approach to model training will succeed before spending the compute budget, which is expensive and often wasted on combinations that don't improve performance.