Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module \textit{Adaptive}-$\eta$, which is compatible with all gradient-based algorithms for offline model-based optimization. Experimental results on DNA/protein sequence design tasks verify the effectiveness of our algorithm. Our code is available~\href{https://anonymous.4open.science/r/BIB-ICLR2023-Submission/README.md}{here.}

翻译：离线模型优化旨在利用静态数据集中的设计方案及其得分，最大化黑箱目标函数。本文聚焦于生物序列设计，以最大化序列得分为目标。近期方法采用双向学习策略，结合前向映射（用于开发）和后向映射（用于约束），并依赖无限宽网络的神经正切核（NTK）构建代理模型。尽管有效，但NTK因其参数化方式无法学习特征，且使用NTK会阻碍整合强大的预训练语言模型（LM），后者可捕获数百万生物序列中丰富的生物物理信息。我们采用另一种代理模型：在预训练LM上添加线性头，并提出线性化方案。这一方法不仅得到闭式损失函数，还充分利用了预训练LM中的生物物理信息。此外，前向映射与后向映射在序列优化中扮演不同角色，因此应赋予不同权重。为实现这一目标，我们训练辅助模型，并通过双层优化框架利用其弱监督信号，有效学习如何平衡两种映射。进一步地，通过扩展该框架，我们开发了首个学习率自适应模块\textit{Adaptive}-$\eta$，该模块兼容所有基于梯度的离线模型优化算法。在DNA/蛋白质序列设计任务上的实验结果验证了算法的有效性。代码已开源：\href{https://anonymous.4open.science/r/BIB-ICLR2023-Submission/README.md}{此处}。