title: Scalable Chain of Thoughts via Elastic Reasoning
summary: Large reasoning models (LRMs) have achieved remarkable progress on complex
  tasks by generating extended chains of thought (CoT). However, their
  uncontrolled output lengths pose significant challenges for real-world
  deployment, where inference-time budgets on tokens, latency, or compute are
  strictly constrained. We propose Elastic Reasoning, a novel framework for
  scalable chain of thoughts that explicitly separates reasoning into two
  phases--thinking and solution--with independently allocated budgets. At test
  time, Elastic Reasoning prioritize that completeness of solution segments,
  significantly improving reliability under tight resource constraints. To train
  models that are robust to truncated thinking, we introduce a lightweight
  budget-constrained rollout strategy, integrated into GRPO, which teaches the
  model to reason adaptively when the thinking process is cut short and
  generalizes effectively to unseen budget constraints without additional
  training. Empirical results on mathematical (AIME, MATH500) and programming
  (LiveCodeBench, Codeforces) benchmarks demonstrate that Elastic Reasoning
  performs robustly under strict budget constraints, while incurring
  significantly lower training cost than baseline methods. Remarkably, our
  approach also produces more concise and efficient reasoning even in
  unconstrained settings. Elastic Reasoning offers a principled and practical
  solution to the pressing challenge of controllable reasoning at scale.
paper_authors._id: 681d7ccb572e742b3f42d1f3
paper_authors.name: Yuhui Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:56:04.644Z
paper_authors._id: 6602869253a0518b2a98cafd
paper_authors.avatarUrl: /avatars/c14b5953a716f42c83ad28147f8308ae.svg
paper_authors.fullname: Yuhui Xu
paper_authors.type: user
paper_authors.user: yuhuixu
paper_authors._id: 681d7ccb572e742b3f42d1f4
paper_authors.name: Hanze Dong
paper_authors.status: admin_assigned
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paper_authors._id: 63a3ff69f91ad3ea5703841d
paper_authors.avatarUrl: /avatars/69227c4bce01d33747c1377b6f9672db.svg
paper_authors.fullname: Hanze Dong
paper_authors.type: user
paper_authors.user: hendrydong
paper_authors._id: 681d7ccb572e742b3f42d1f5
paper_authors.name: Lei Wang
paper_authors._id: 681d7ccb572e742b3f42d1f6
paper_authors.name: Doyen Sahoo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:56:18.676Z
paper_authors._id: 65f84fd980481173afd91233
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paper_authors.fullname: Doyen
paper_authors.type: user
paper_authors.user: doyensahoo
paper_authors._id: 681d7ccb572e742b3f42d1f7
paper_authors.name: Junnan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:56:32.272Z
paper_authors._id: 61f9d3b54ac99e8a1bae85f4
paper_authors.avatarUrl: /avatars/ac47d13204dd22452e4bc46e280842d5.svg
paper_authors.fullname: JunnanLi
paper_authors.type: user
paper_authors.user: JunnanLi
paper_authors._id: 681d7ccb572e742b3f42d1f8
paper_authors.name: Caiming Xiong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:56:38.430Z
paper_authors._id: 649dbcc4e0fff1ed099dc80a
paper_authors.avatarUrl: /avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
paper_authors.fullname: Caiming Xiong
paper_authors.type: user
paper_authors.user: cxiong
paper_title: Scalable Chain of Thoughts via Elastic Reasoning
paper_summary: Large reasoning models (LRMs) have achieved remarkable progress on complex
  tasks by generating extended chains of thought (CoT). However, their
  uncontrolled output lengths pose significant challenges for real-world
  deployment, where inference-time budgets on tokens, latency, or compute are
  strictly constrained. We propose Elastic Reasoning, a novel framework for
  scalable chain of thoughts that explicitly separates reasoning into two
  phases--thinking and solution--with independently allocated budgets. At test
  time, Elastic Reasoning prioritize that completeness of solution segments,
  significantly improving reliability under tight resource constraints. To train
  models that are robust to truncated thinking, we introduce a lightweight
  budget-constrained rollout strategy, integrated into GRPO, which teaches the
  model to reason adaptively when the thinking process is cut short and
  generalizes effectively to unseen budget constraints without additional
  training. Empirical results on mathematical (AIME, MATH500) and programming
  (LiveCodeBench, Codeforces) benchmarks demonstrate that Elastic Reasoning
  performs robustly under strict budget constraints, while incurring
  significantly lower training cost than baseline methods. Remarkably, our
  approach also produces more concise and efficient reasoning even in
  unconstrained settings. Elastic Reasoning offers a principled and practical
  solution to the pressing challenge of controllable reasoning at scale.
paper_ai_summary: Elastic Reasoning is a framework that divides reasoning into thinking and solution phases with separate budgets, improving model reliability and efficiency under resource constraints.
paper_ai_keywords: Elastic Reasoning chain of thoughts GRPO budget-constrained rollout strategy