title: AdaCtrl: Towards Adaptive and Controllable Reasoning via
    Difficulty-Aware Budgeting
summary: 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.
paper_authors._id: 683528d8c682e155a8b9a80f
paper_authors.name: Shijue Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T10:03:37.508Z
paper_authors._id: 64ce05c631c655ff8a2e183c
paper_authors.avatarUrl: /avatars/f2de7f8a1348b05f46946085e3e9718e.svg
paper_authors.fullname: Shijue Huang
paper_authors.type: user
paper_authors.user: JoeYing
paper_authors._id: 683528d8c682e155a8b9a810
paper_authors.name: Hongru Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:07:34.709Z
paper_authors._id: 65f906e5c3dbdcae83ff7aac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65f906e5c3dbdcae83ff7aac/mdjiVkLDJgJcGLwv0rMe4.jpeg
paper_authors.fullname: Hongru Wang
paper_authors.type: user
paper_authors.user: Merlin-Hongru
paper_authors._id: 683528d8c682e155a8b9a811
paper_authors.name: Wanjun Zhong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:07:08.589Z
paper_authors._id: 643f956635e2b54a42e7feba
paper_authors.avatarUrl: /avatars/c6185f81ae8499ae866ad451c1cbf43b.svg
paper_authors.fullname: Wanjun Zhong
paper_authors.type: user
paper_authors.user: WanjunZhong
paper_authors._id: 683528d8c682e155a8b9a812
paper_authors.name: Zhaochen Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:07:15.418Z
paper_authors._id: 64264095ba51f8a2136946a0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64264095ba51f8a2136946a0/FR33boVpkDXcrvGMBmprF.jpeg
paper_authors.fullname: Zhaochen Su
paper_authors.type: user
paper_authors.user: Warrieryes
paper_authors._id: 683528d8c682e155a8b9a813
paper_authors.name: Jiazhan Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T09:40:28.530Z
paper_authors._id: 649a95f951e1ea30f1475530
paper_authors.avatarUrl: /avatars/d4d167c3985e702044e8290408dde661.svg
paper_authors.fullname: Jamie Jiazhan Feng
paper_authors.type: user
paper_authors.user: jzfeng
paper_authors._id: 683528d8c682e155a8b9a814
paper_authors.name: Bowen Cao
paper_authors._id: 683528d8c682e155a8b9a815
paper_authors.name: Yi R. Fung
paper_title: AdaCtrl: Towards Adaptive and Controllable Reasoning via
    Difficulty-Aware Budgeting
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: AdaCtrl adaptive reasoning budget allocation self-assessed problem difficulty difficulty-aware reinforcement learning two-stage training pipeline cold-start fine-tuning