The core challenge in deploying advanced LLMs, particularly in agentic systems, lies in reliably controlling their behavior under conflicting instructions. Ensuring that system-level directives take precedence over user or tool commands is paramount for safety and security, yet notoriously difficult to train. Failures in this critical area can manifest as jailbreaks, prompt injections, or simply an inability to follow essential system constraints. The researchers introduce IH-Challenge, a new dataset designed to tackle these complexities head-on, enabling more robust instruction hierarchy LLM training.
Tackling Nuanced Instruction Conflicts with IH-Challenge
Achieving robust instruction hierarchy (IH) in LLMs is a significant hurdle. Conflicts are often subtle, and models can learn undesirable shortcuts like overrefusal, confounding true instruction-following capabilities with IH failures. To address this, IH-Challenge was developed. This dataset, coupled with reinforcement learning and online adversarial example generation, allows for targeted training of LLM behavior when faced with competing instructions from system, developer, user, and tool sources. The researchers demonstrated that fine-tuning GPT-5-Mini on IH-Challenge led to a remarkable +10.0% average improvement in IH robustness across a range of benchmarks, including in-distribution, out-of-distribution, and human red-teaming scenarios, pushing performance from 84.1% to 94.1%.
