AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting

Understand

Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies.

• However, they often struggle to balance efficiency and effectiveness, frequently generating unnecessarily lengthy reasoning chains for simple problems.
• In this work, we propose AdaCtrl, a novel framework to support both difficulty-aware adaptive reasoning budget allocation and explicit user control over reasoning depth.
• AdaCtrl dynamically adjusts its reasoning length based on self-assessed problem difficulty, while also allowing users to manually control the budget to prioritize either efficiency or effectiveness.