  Dealing with multi-task trade-offs during inference can be addressed via
Pareto Front Learning (PFL) methods that parameterize the Pareto Front with a
single model, contrary to traditional Multi-Task Learning (MTL) approaches that
optimize for a single trade-off which has to be decided prior to training.
However, recent PFL methodologies suffer from limited scalability, slow
convergence and excessive memory requirements compared to MTL approaches while
exhibiting inconsistent mappings from preference space to objective space. In
this paper, we introduce PaLoRA, a novel parameter-efficient method that
augments the original model with task-specific low-rank adapters and
continuously parameterizes the Pareto Front in their convex hull. Our approach
dedicates the original model and the adapters towards learning general and
task-specific features, respectively. Additionally, we propose a deterministic
sampling schedule of preference vectors that reinforces this division of labor,
enabling faster convergence and scalability to real world networks. Our
experimental results show that PaLoRA outperforms MTL and PFL baselines across
various datasets, scales to large networks and provides a continuous
parameterization of the Pareto Front, reducing the memory overhead $23.8-31.7$
times compared with competing PFL baselines in scene understanding benchmarks.
