Steering LLMs via Scalable Interactive Oversight

TL;DR

This paper introduces Scalable Interactive Oversight, a framework that breaks down complex tasks into manageable decisions, enabling non-experts to effectively guide large language models and improve alignment through recursive feedback and reinforcement learning.

Contribution

The paper presents a novel oversight framework that decomposes complex tasks into decision trees, allowing scalable human supervision and reinforcement learning optimization.

Findings

Achieved 54% improvement in task alignment with web development.

Enabled non-experts to produce expert-level product requirement documents.

Demonstrated reinforcement learning can optimize oversight using online user feedback.

Abstract

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.