Reinforcement learning from human feedback (RLHF) has contributed to performance improvements in large language models. To tackle its reliance on substantial amounts of human-labeled data, a successful approach is multi-task representation learning, which involves learning a high-quality, low-dimensional representation from a wide range of source tasks. In this paper, we formulate RLHF as the contextual dueling bandit problem and assume a common linear representation. We demonstrate that the sample complexity of source tasks in multi-task RLHF can be reduced by considering task relevance and allocating different sample sizes to source tasks with varying task relevance. We further propose an algorithm to estimate task relevance by a small number of additional data and then learn a policy. We prove that to achieve $\varepsilon-$optimal, the sample complexity of the source tasks can be significantly reduced compared to uniform sampling. Additionally, the sample complexity of the target task is only linear in the dimension of the latent space, thanks to representation learning.

通过多任务表示学习的方式，我们将人类反馈强化学习 (RLHF) 问题建模为一种上下文二分问题，并假设存在一种共同线性表示。我们证明了考虑任务相关性，并为具有不同任务相关性的源任务分配不同样本数量可以降低多任务 RLHF 中的样本复杂度。此外，由于表示学习，目标任务的样本复杂度仅与潜在空间的维度成线性关系。

强化学习中主动多任务学习的力量