Engineering and algorithmic interventions for multimodal post-training at Microsoft scale
Here's a 3-sentence summary of the Microsoft engineering blog post on multimodal post-training interventions at scale: To address degradation in multimodal post-training at Microsoft scale, engineers developed five interventions: a staged objective curriculum, adaptive curriculum from estimator health, variance-corrected normalization, and techniques to improve advantage estimates and latent reward learning. The staged curriculum prevents premature specialization by anchoring early learning with entropy and introducing preference signals later, while the adaptive curriculum detects and addresses estimator health and effective sample size issues through near-miss trajectory injection. These interventions improved the reliability and performance of production models by addressing issues like trajectory bias, gradient signal concentration, and failure to handle heterogeneity and scale.