Reinforcement Learning with Verifiable Rewards Makes Models Faster, Not Smarter

RLVR (Reinforcement Learning with Verifiers) is a training method that enhances model performance primarily through search compression, concentrating its probability distribution over paths the base model can already sample. It utilizes programmatic verifiers instead of learned reward models, providing deterministic feedback and eliminating the need for extensive reward model training. This approach is particularly effective for tasks with clear ground truths but faces challenges in creative and subjective domains. RLVR's efficiency gains are largely attributed to improved sampling, where it enhances pass-at-1 rates without significantly lifting the pass-at-k ceiling, indicating minimal expansion of reasoning capabilities. Key challenges include verifier design, which can lead to exploitable gaps if not comprehensive, and entropy instability during training, which can hinder generalization. Despite these challenges, RLVR offers a cost-effective alternative to RLHF for tasks with objective correctness, though it is essential to validate gains across different model families and ensure the verifiers' robustness. The method's potential lies in its ability to optimize search rather than fundamentally expanding a model's intelligence, highlighting the importance of assessing whether performance improvements are due to true learning or merely more efficient sampling.