A simple sequence-design trick: explore beyond training data, then penalize bad candidates
anshulkundaje · x · 2026-07-29
A repost of a sequence-design idea: let the model explore beyond what it has seen in training, then explicitly penalize the outputs that are bad. The note says this works especially well for sequence design, where most candidate sequences are invalid or unpromising.
The core takeaway is an optimization strategy for generative search: encourage novelty, but use a strong scoring or penalty step to filter out the large bad space. The comment implies that this framing is particularly effective in domains like biological sequence design.
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