LASER targets rare safety failures with active learning

What happenedOpenAI describes LASER, a pipeline combining embedding-based classifiers, uncertainty sampling, reasoning-model labeling, and diversity selection. It reports much lower grading compute than random sampling for finding comparable numbers of rare disallowed examples.

Engineering perspective · analysisUse targeted sampling to build challenging evaluation sets economically, but keep a separate representative sample when estimating production failure rates. Validate model-generated labels before using them as ground truth.

Limits of the evidenceThe efficiency result applies to a specific rare-event sampling objective. It is not a universal inference-cost improvement. A boundary-focused test set does not measure real-world failure prevalence.