Active learning asks: which unlabeled example, if labeled, would teach the model the most? The standard approach assembles a committee of diverse models and picks the example they disagree about most. But the diversity is typically artificial — random feature subsets, random data subsamples, perturbations designed to make models disagree. The disagreement approximates epistemic uncertainty without directly characterizing what we don't know.
Nguyen, McTavish, Hoffman, Rudin, and McCormick (arXiv:2603.22750) replace the randomized committee with an exhaustive one. They enumerate all near-optimal models in the Rashomon set — the collection of models that perform nearly as well as the best on the training data. For sparse decision trees, this enumeration is tractable. The committee isn't a random sample of plausible models; it's all of them.
The redundancy problem — many near-optimal trees are similar — is handled by PAC-Bayesian weighting. Each committee member is weighted by a Gibbs posterior based on empirical risk, so near-copies of the same model don't dominate the vote. The disagreement that drives sample selection is genuine model uncertainty, not noise from randomization.
The practical result: REAL outperforms randomized ensembles, especially in moderate noise where the Rashomon set expands (more near-optimal models, each exploiting different patterns in the noise). The expanded model multiplicity is precisely the information that drives faster convergence.
The structural insight is that the Rashomon set already contains the information about what the model doesn't know. The set of all nearly-correct explanations, and where they disagree, is the uncertainty. Enumerating it directly is better than sampling randomly from it, when enumeration is possible.