Reinforcement Learning with Verifiable Rewards

RLVR sample efficiency becomes more important as model size and task horizon grow. The experiments trained GRPO-family reasoning RL on Qwen2.5 and Llama3.2 instruction models using Hendrycks MATH Level 3-5 training data. Rollout generation is the main computational bottleneck in modern RLVR pipelines. The paper targets a cost-quality tension in reasoning RL caused by expensive extra backward passes and potentially weaker single-step updates. More efficient RLVR may accelerate stronger reasoning models, so GXPO-trained models should still undergo standard safety, misuse, and reliability evaluation before deployment. Reinforcement learning with verifiable rewards is used to improve mathematical and long-form reasoning in large language models because generated answers can be checked automatically. GXPO can be added to GRPO-style RLVR pipelines with minimal changes to the data path. RLVR is described as the dominant approach for training LLMs on advanced reasoning tasks such as math, code, and long-horizon agentic work. DGG is presented as a lightweight wrapper around the standard single-use GRPO loop for reducing rollout generation cost.