Fireworks AI explores the potential of Reinforcement Learning with Verifiable Reward (RLVR) as a promising approach to enhance AI model performance without relying on fully labeled data, focusing on the GRPO (Group Relative Policy Optimization) algorithm. Unlike the traditional PPO (Proximal Policy Optimization) algorithm, GRPO eliminates the need for a Value Model, reducing computational burden and simplifying training. The DeepSeek R1-Zero model, trained with GRPO, demonstrates the ability to self-evolve and solve complex tasks without supervised training data, relying on a Verifiable Reward Function that uses predefined rules to evaluate model outputs. Experiments conducted by the Fireworks AI team highlight the effectiveness of RLVR, achieving significant improvements in tasks like digit multiplication and function picking, showcasing its potential for rapid model fine-tuning and optimization across various domains. Fireworks AI positions itself as a leading provider of enterprise-scale LLM inference engines, offering solutions for building low-latency, high-performance generative AI applications with a focus on cost efficiency and open-source integration.