AdaCtrl: Towards Adaptive and Controllable Reasoning via
  Difficulty-Aware Budgeting
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.
683528d8c682e155a8b9a80f
Shijue Huang
claimed_verified
2025-05-27T10:03:37.508Z
64ce05c631c655ff8a2e183c
/avatars/f2de7f8a1348b05f46946085e3e9718e.svg
Shijue Huang
user
JoeYing
683528d8c682e155a8b9a810
Hongru Wang
admin_assigned
2025-05-27T13:07:34.709Z
65f906e5c3dbdcae83ff7aac
https://cdn-avatars.huggingface.co/v1/production/uploads/65f906e5c3dbdcae83ff7aac/mdjiVkLDJgJcGLwv0rMe4.jpeg
Hongru Wang
user
Merlin-Hongru
683528d8c682e155a8b9a811
Wanjun Zhong
admin_assigned
2025-05-27T13:07:08.589Z
643f956635e2b54a42e7feba
/avatars/c6185f81ae8499ae866ad451c1cbf43b.svg
Wanjun Zhong
user
WanjunZhong
683528d8c682e155a8b9a812
Zhaochen Su
admin_assigned
2025-05-27T13:07:15.418Z
64264095ba51f8a2136946a0
https://cdn-avatars.huggingface.co/v1/production/uploads/64264095ba51f8a2136946a0/FR33boVpkDXcrvGMBmprF.jpeg
Zhaochen Su
user
Warrieryes
683528d8c682e155a8b9a813
Jiazhan Feng
claimed_verified
2025-05-29T09:40:28.530Z
649a95f951e1ea30f1475530
/avatars/d4d167c3985e702044e8290408dde661.svg
Jamie Jiazhan Feng
user
jzfeng
683528d8c682e155a8b9a814
Bowen Cao
683528d8c682e155a8b9a815
Yi R. Fung
AdaCtrl: Towards Adaptive and Controllable Reasoning via
  Difficulty-Aware Budgeting
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.
AdaCtrl, a novel framework, dynamically adjusts reasoning length based on problem difficulty and user control, improving performance and reducing response length across various datasets compared to standard training methods.
AdaCtrl adaptive reasoning budget allocation self-assessed problem difficulty difficulty-aware reinforcement learning two-stage training pipeline cold-start fine-tuning