Scalable Chain of Thoughts via Elastic Reasoning
Authors: name: Yuhui Xu, name: Hanze Dong, name: Lei Wang, name: Doyen Sahoo, name: Junnan Li, name: Caiming Xiong

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.