Verifiable Rewards: How AWS Is Improving Neural Network Training

AWS has introduced a new approach to reinforcement learning, Reinforcement Learning with Verifiable Rewards (RLVR), which brings verification and transparency to reward signals. Instead of approximate scores for answer quality, the model receives a reward only if the result is completely correct and can be objectively checked.

The problem with traditional RL

In standard reinforcement learning, the reward function scores the quality of the model's action. But these scores are often imprecise: it is hard to design a function that correctly rates complex behavior. The model may end up optimizing for the wrong thing, an effect known as reward hacking.

RLVR tackles this problem at its root: a reward is issued only for a fully correct result. This is possible for tasks where the answer can be checked unambiguously. The model learns from ground truth, not from approximate scores.

Where verifiable rewards work

Verification applies wherever a result has an objective criterion:

• Mathematical reasoning — proving theorems, solving equations. An answer is either mathematically correct or it is not
• Code generation — a parser checks syntax, and tests check functionality. There is no room for subjectivity
• Symbolic manipulation — logical transformations, algebra. Checking can be fully automated
• Structured data extraction — if the task has a correct format, it is easy to validate

RLVR is less effective for tasks without objective verification, such as text generation or design.

GRPO + few-shot learning

AWS adds Group Relative Policy Optimization (GRPO) to RLVR, a modification of the policy optimization algorithm. Rather than improving each step independently, GRPO groups sequences of actions and compares them against each other. This speeds up convergence and avoids local minima.

An additional layer is few-shot examples. The model first sees a few solved examples (usually 3–5) and then trains on the full dataset. This helps lock in the desired behavior pattern before optimization begins.

The combination works synergistically: verifiable rewards provide a clean signal, GRPO speeds up the search for the optimum, and few-shot examples fix the format.

Results on GSM8K

AWS tested the approach on GSM8K, a set of 8500 grade-school math problems of varying difficulty. The model trained with RLVR showed a significant gain in solution accuracy compared with baseline methods.

The key point: verification is built into the training process rather than added as a check at the end. This lets the model learn from correct examples instead of trying to please an approximate reward function.

The methodology transfers well to neighboring domains: code generation, logic checking, configuration validation.

What this means

Verifiable rewards shift the core of training from heuristics to verification. For engineers and researchers: if your task allows objective verification, RLVR should deliver higher accuracy and fewer strange artifacts. AWS is preparing this approach for scaling through SageMaker AI, which will make it easier for cloud users to adopt.