Automated detection of contradictions in 5G network specifications using reinforcement learning-trained small LLM

Contradictions in telecommunications specifications can lead to implementation errors, interoperability issues, and security vulnerabilities. This paper presents a framework for automating the detection of contradictions in telecommunications standards using a 3B parameter large language model (LLM) trained via reinforcement learning. We develop a formal taxonomy of contradiction types in 5G specifications, generate high-quality synthetic training data using carefully designed prompts to larger LLMs, and train a specialized model using group relative policy optimization (GRPO) with a multi-component reward function. Our experiments demonstrate that our RL-trained small model achieves superior performance compared to larger general models on both synthetic and real-world contradictions from the CellularLint dataset. The effectiveness of our approach has significant implications for internet of things (IoT) ecosystems, where consistent and reliable network specifications are crucial for ensuring interoperability, power efficiency, and security across billions of connected devices that rely on 5G infrastructure. Our work demonstrates that specialized smaller models can exceed the capabilities of larger general models on domain-specific tasks, offering a resource-efficient approach to specification verification that could accelerate the development and deployment of reliable IoT systems.