AdaCtrl: Towards Adaptive and Controllable Reasoning via
  Difficulty-Aware Budgeting
Authors: name: Shijue Huang, name: Hongru Wang, name: Wanjun Zhong, name: Zhaochen Su, name: Jiazhan Feng, name: Bowen Cao, name: Yi R. Fung

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. This is achieved through a
two-stage training pipeline: an initial cold-start fine-tuning phase to instill
the ability to self-aware difficulty and adjust reasoning budget, followed by a
difficulty-aware reinforcement learning (RL) stage that refines the model's
adaptive reasoning strategies and calibrates its difficulty assessments based
on its evolving capabilities during online training. To enable intuitive user
interaction, we design explicit length-triggered tags that function as a
natural interface for budget control. Empirical results show that AdaCtrl
adapts reasoning length based on estimated difficulty, compared to the standard
training baseline that also incorporates fine-tuning and RL, it yields
performance improvements and simultaneously reduces response length by 10.06%
and 12.14% on the more challenging AIME2024 and AIME2025 datasets, which
require elaborate reasoning, and by 62.05% and 91.04% on the MATH500 and GSM8K
datasets, where more concise responses are sufficient. Furthermore, AdaCtrl
enables precise user control over the reasoning budget, allowing for tailored
responses to meet specific needs.