Quantifying Empirical Compute-Supervision Tradeoffs in RLVR 事件

Quantifying Empirical Compute-Supervision Tradeoffs in RLVR arXiv:2605.25252v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training language models, but in practice, verifiers are rarely perfect. Recent theoretical work predicts that verifier noise affects the rate of learning but not its final outcome, implying that sufficient compute should close any gap induced by imperfect supervision. We test this prediction e