Models can learn to self-verify and adaptively allocate test-time compute by treating their own correctness predictions as a control signal, eliminating the need for external verifiers while improving efficiency.
SVR teaches language models to verify their own answers during inference and decide when to stop refining, without needing external feedback. The model learns to produce solutions with confidence scores and correctness verdicts, continuing to refine only when uncertain.