Theory Explains Why Unsupervised RLVR Collapses on Large Datasets
The paper turns a known failure mode of self-rewarding training into a predictive formula, giving practitioners a way to estimate safe dataset sizes before a run collapses. It also identifies two concrete mitigations, removing the shared coupling direction or adding a marginal-entropy bonus, that were validated on an actual Transformer. Researchers working on verifiable-reward training and anyone training reasoning models without labels will find the scaling laws directly applicable.