Steering LLMs via Scalable Interactive Oversight
This addresses the scalable oversight challenge for users who need to steer AI systems on tasks beyond their own specification or verification capabilities, representing a novel method for a known bottleneck.
The paper tackles the problem of humans struggling to effectively guide Large Language Models on complex tasks due to insufficient expertise and difficulty articulating intent, proposing a Scalable Interactive Oversight framework that decomposes intent into manageable decisions with low-burden feedback. In web development tasks, this enabled non-experts to produce expert-level Product Requirement Documents with a 54% improvement in alignment.
As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often struggle to guide them effectively due to insufficient domain expertise, the difficulty of articulating precise intent, and the inability to reliably validate complex outputs. It presents a critical challenge in scalable oversight: enabling humans to responsibly steer AI systems on tasks that surpass their own ability to specify or verify. To tackle this, we propose Scalable Interactive Oversight, a framework that decomposes complex intent into a recursive tree of manageable decisions to amplify human supervision. Rather than relying on open-ended prompting, our system elicits low-burden feedback at each node and recursively aggregates these signals into precise global guidance. Validated in web development task, our framework enables non-experts to produce expert-level Product Requirement Documents, achieving a 54\% improvement in alignment. Crucially, we demonstrate that this framework can be optimized via Reinforcement Learning using only online user feedback, offering a practical pathway for maintaining human control as AI scales.