GPA: Generative Population Annealing for Test-Time Sequence Design

Anirban Sarkar, Alejandra Duran, Peter K Koo

Keywords: Sequential Monte Carlo, Population annealing

TL;DR: GPA samples a frozen pretrained generator at population scale to design DNA sequences against any oracle, with no fine-tuning, matching or beating RL and tree-search baselines at a fraction of the wall-time.

Abstract: Oracle-guided biological sequence design must improve predicted function without moving outside the sequence distribution where the oracle is trustworthy. We introduce Generative Population Annealing (GPA), a test-time sampler for sequence design with a frozen generator and frozen oracle. GPA instantiates annealed Sequential Monte Carlo at population scale: particles are initialized from a pretrained sequence prior, reweighted by an oracle reward tilt, selectively upsampled as effective sample size falls, mutated with the pretrained generator, and returned as the design pool. The base sampler targets a reward-tilted prior; practical variants shape the proposal or modify the objective to trade activity, specificity, fidelity, and diversity. Across enhancer and promoter benchmarks, GPA scales to thousands of sequences and is competitive with inference-time samplers, gradient-based editors, tree-search diffusion, and RL-fine-tuned generators. The same inference loop is evaluated with masked discrete-diffusion and autoregressive DNA generators. GPA reaches high predicted activity while preserving strong motif fidelity and model-based likelihood, although specialized baselines retain higher diversity or stronger $k$-mer fidelity in some settings. Cross-oracle and sequence-level audits suggest fewer obvious reward-hacking pathologies, including shorter homopolymer runs than CTRL-DNA in the HepG2 audit (5.1 bp mean maximum run versus 20.9 bp), but all validation remains computational.

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Submission Number: 143