AI BriefingImproving the Instruction Hierarchy of Frontier LLMs
Key point
OpenAI released IH-Challenge, a reinforcement learning dataset that helps models correctly judge priority among conflicting instructions.
Details
AI systems receive instructions from a variety of sources, including system messages, developer guides, user requests, and online data. When the priority order among these breaks down, serious safety issues arise, such as security policy violations or prompt injection attacks.
To address this, it is essential to establish a clear Instruction Hierarchy in the order System > Developer > User > Tool. As long as constraints from higher levels do not conflict, the model should follow instructions from lower levels.
The newly designed IH-Challenge is a dataset for reinforcement learning (RL), designed to overcome three pitfalls of existing training methods. It simplifies instructions to increase the model's comprehension, enables objective grading via Python scripts, and prevents the model from learning the shortcut of over-refusal, where it indiscriminately refuses to answer for the sake of safety.
Through this approach, the internally trained model GPT-5 Mini-R showed improved performance on instruction hierarchy benchmarks and demonstrated strong resistance to prompt injection attacks. It also achieved results that were more responsive to safety guidelines while maintaining the model's overall usefulness.
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