self-verifying-refinement-b4837e39·1 events·first seen Aliases: Self-Verifying Refinement
Researchers introduce Self-Verifying Refinement (SVR), a multi-turn reinforcement learning framework that trains language models to self-verify their own outputs and adaptively stop refinement without external verifiers. The model produces a solution, a correctness verdict, and a confidence score at each turn, continuing refinement only when uncertain; ground-truth labels are used only for reward construction, not at inference. Trained with GRPO on Qwen3.5-2B, SVR achieves competitive accuracy on seven math reasoning benchmarks using an average of only 2.99 inference turns, outperforming fixed-budget and oracle-guided baselines. The work advances the line of research on efficient, oracle-free test-time compute scaling.