Steering LLMs via Scalable Interactive Oversight
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918325725954048 |
|---|---|
| author | Zhou, Enyu Xi, Zhiheng Ma, Long Zhang, Zhihao Dou, Shihan Lei, Zhikai Wang, Guoteng Zheng, Rui Yan, Hang Gui, Tao Zhang, Qi Huang, Xuanjing |
| author_facet | Zhou, Enyu Xi, Zhiheng Ma, Long Zhang, Zhihao Dou, Shihan Lei, Zhikai Wang, Guoteng Zheng, Rui Yan, Hang Gui, Tao Zhang, Qi Huang, Xuanjing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04210 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Steering LLMs via Scalable Interactive Oversight Zhou, Enyu Xi, Zhiheng Ma, Long Zhang, Zhihao Dou, Shihan Lei, Zhikai Wang, Guoteng Zheng, Rui Yan, Hang Gui, Tao Zhang, Qi Huang, Xuanjing Artificial Intelligence Machine Learning 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. |
| title | Steering LLMs via Scalable Interactive Oversight |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.04210 |