Reinforcement Learning from Verifiable Rewards

Train reasoning models using binary or structured reward signals from verifiable outcomes (correct answer, pass@k, compiler output) instead of learned reward models. RLVR replaces the learned reward model with a deterministic verifier that checks whether a response satisfies a ground-truth outcome. For math, this means checking if the final answer matches. For code, it means running test cases. For agents, it means success/failure on a task. The verifier provides a clean, binary reward signal that eliminates reward hacking and reduces the complexity of the training pipeline. Combined with GRPO or PPO, RLVR enables scalable training for reasoning.