Reinforcement Learning from Verifiable Rewards

The model samples completions for tasks whose results can be evaluated without a learned human preference model. A verifier returns a reward based on properties such as answer equivalence, executable tests or valid output structure. Policy optimization increases the probability of rewarded completions; different reinforcement learning algorithms can perform this update. Outcome verification checks the final result and does not necessarily establish that an explanation is sound. Format rewards may supplement correctness rewards but represent a separate objective. The approach is most natural when evaluation is inexpensive, reproducible and difficult for the model to game, though these conditions need testing.

Build and version the verifier before scaling policy training. Inspect parsing, numerical tolerances, test coverage, timeouts and adversarial outputs that might receive credit incorrectly. Separate problem generators, templates and solutions across training and evaluation. Sample tasks at a difficulty that produces informative variation rather than uniformly zero or maximum reward. Track reward distributions and evaluate on independently checked problems. The deliverable includes a trained policy, a reproducible verification harness and evidence distinguishing actual task success from improvements caused by formatting or a weak checker.

Verification can be incomplete: tests miss behaviors, answer parsers accept ambiguous strings and numerical comparisons mishandle units. Sparse rewards make exploration difficult, while permissive checks invite shortcuts. Success on automatically checkable tasks does not establish quality on subjective assistance or open-ended factual claims. Correct answers also need not imply faithful reasoning traces. Treat verifier reliability and independent task accuracy as separate quality measures, and reassess them when the policy discovers new output strategies.