Gemma 4 31B-IT Model Achieves Operator-Level Performance on Nuclear Licensing Exams

A new study benchmarks the performance of a 31-billion-parameter multimodal language model, Gemma 4 31B-IT, on the U.S. Nuclear Regulatory Commission's Reactor Operator licensing examination. The research evaluated eight different model-retrieval configurations, finding that supervised fine-tuning combined with retrieval-augmented generation using a fixed-size chunking strategy met the human passing criterion on 8 out of 14 examinations. This approach achieved an aggregate accuracy of 79.7%, demonstrating that specific fine-tuning and retrieval methods can enable LLMs to perform at an operator level in this domain. AI

IMPACT Demonstrates potential for LLMs to achieve operator-level capabilities in specialized, high-stakes domains like nuclear power.

RANK_REASON Academic paper detailing model performance on a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Gemini
• Gemma 4 31B-IT
• NRC Reactor Operator licensing examination
• Nuclear Regulatory Commission
• U.S. Department of Energy Fundamentals Handbook

• paper
• model release

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