Sulong Xu

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.

Even advanced LLMs struggle with budget-constrained combo shopping, revealing a significant gap in their ability to handle real-world constraints effectively.