There Is No Free Lunch in RLVR: Every pass@1 Gain Has a pass@k Price
arXiv:2610.39295v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) makes a language model solve more problems on the first try and fewer when given many tries: it raises pass@1 while the base model overtakes it at large sampling budget k. Is this a failure of RL to add reasoning, or a transient that more training removes? We settle the question with a theorem. The reversal is a price, and we compute its exact rate. Modeling RLVR as an exponential tilt of a b…