Subham Rath

What pass@k Cannot Measure: Evaluating Diversity and Capability Retention after Post-Training

pass@$k$, the fraction of problems a model solves within $k$ sampled attempts, is the field's default protocol for deciding whether reinforcement-learning (RL) post-training on verifiable rewards improved a model. At the population level, pass@$k$ depends only on a problem's probability of a correct sample, with no ter...