TL;DR of the paper, generated automatically.

LambdaMix: one training run gives a whole family of trade-off policies.

• Method: one low-rank adapter per objective, on a frozen base model.
• Control: a preference vector picks the adapter mix, no retraining at inference.
• Results: higher hypervolume than baselines on four models, with fewer training completions.