CRAFT: A Candidate-Retention Framework for Offline-to-Online Sequence Design

Designing biological sequences under a limited experimental budget requires selecting a small number of candidates for evaluation. Even when candidate generation produces high-performing sequences, underestimation of their performance may prevent their selection for experimental evaluation. We propose CRAFT, an offline-to-online framework that separates candidate generation from experimental selection and uses accumulated measurements to guide both. Starting from an offline dataset, it constructs candidate pools via measurement-guided search, including local mutations of high-scoring observed sequences. A separate selector applies the same scoring rule to candidates from all sources, using an upper quantile of the measured property values of their nearest neighbors, and forms evaluation batches favoring high scores and sequence diversity. After each round, new measurements update candidate generation and scoring, while recent performance determines how future evaluations are allocated among candidate sources. Theoretically, for fixed candidate pools under bounded scoring error, we derive an upper bound on the gap in top-\(K\) mean objective value between the candidate pool and the evaluated batch, and relate this gap to cumulative improvement across rounds. Across six benchmarks spanning RNA, DNA, and protein design, CRAFT achieves the highest mean top-128 score on five tasks and ranks second on GFP under matched online query budgets. Our code is available at https://anonymous.4open.science/r/craft-code-private-3C88/.