Gu Simiu

Naive linear reward scalarization inevitably triggers destructive gradient tug-of-wars, but decoupling marginal objectives via orthogonal projections and localized constraints stabilizes training across both recommender systems and complex LLM alignment tasks.

Textual shortcuts in VLMs can significantly undermine reasoning accuracy, but a simple intervention can restore fresh visual computation.