Building Verifiable Rewards for Reasoning Models

This article introduces Reinforcement Learning with Verifiable Rewards (RLVR), a powerful approach for training advanced reasoning models, including large language models, by using objective, programmatic reward signals.

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RLVR marks a significant advancement in machine learning, proving profoundly impactful for training advanced reasoning models, especially Large Language Models (LLMs). It guides their learning towards objectively correct outputs, fostering reasoning capabilities and pushing models beyond linguistic fluency to genuine problem-solving proficiency. RLVR fundamentally departs from methods relying on subjective human feedback, such as Reinforcement Learning from Human Feedback (RLHF), and instead hinges on reward signals that are programmatically verifiable. This means the feedback loop provides deterministic, rule-based assessments of correctness, eliminating ambiguity. RLVR's emphasis remains squarely on correctness, not vague human inclinations. Building an RLVR system from scratch follows a structured workflow, including defining the task, generating training data, designing the verifier, assigning verifiable rewards, and optimizing the policy.