  Foundation models are first pre-trained on vast unsupervised datasets and
then fine-tuned on labeled data. Reinforcement learning, notably from human
feedback (RLHF), can further align the network with the intended usage. Yet the
imperfections in the proxy reward may hinder the training and lead to
suboptimal results; the diversity of objectives in real-world tasks and human
opinions exacerbate the issue. This paper proposes embracing the heterogeneity
of diverse rewards by following a multi-policy strategy. Rather than focusing
on a single a priori reward, we aim for Pareto-optimal generalization across
the entire space of preferences. To this end, we propose rewarded soup, first
specializing multiple networks independently (one for each proxy reward) and
then interpolating their weights linearly. This succeeds empirically because we
show that the weights remain linearly connected when fine-tuned on diverse
rewards from a shared pre-trained initialization. We demonstrate the
effectiveness of our approach for text-to-text (summarization, Q&A, helpful
assistant, review), text-image (image captioning, text-to-image generation,
visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the
alignment of deep models, and how they interact with the world in all its
diversity.
