OmniOData: Unleashing Small Language Models for OData Query Generation with Synthetic Data and Reinforcement Learning
Despite the success of Large Language Models (LLMs) in structured query generation, OData—a critical RESTful protocol for enterprise APIs—remains under-researched due to a lack of high-fidelity, execution-validated datasets. To bridge this gap, we introduce O M - NI OD ATA , a framework that generates S YN O-D ATA , the first large-scale OData corpus featuring execution-grounded queries and reasoning traces. Using this corpus, we develop O MNI OD ATA -R1 (1.5B–3B parameters), a family of models that match or surpass frontier proprietary systems, such as GPT-4o and Gemini 3, on realistic industrial benchmarks. Our results demonstrate that the synergy of execution-verified synthetic data and Reinforcement Learning (RL) effectively unlocks the latent reasoning of Small Language Models (SLMs), providing a high-performance, low-latency solution for specialized enterprise query generation. The code and data will be released under an open-source license.