Optimizing Length Compression in Large Reasoning Models
Authors: name: Zhengxiang Cheng, name: Dongping Chen, name: Mingyang Fu, name: Tianyi Zhou

Large Reasoning Models (LRMs) have achieved remarkable success, yet they
often suffer from producing unnecessary and verbose reasoning chains. We
identify a core aspect of this issue as "invalid thinking" -- models tend to
repeatedly double-check their work after having derived the correct answer. To
address this specific inefficiency, we move beyond the general principles of
Efficacy and Efficiency to propose two new, fine-grained principles: Brevity,
which advocates for eliminating redundancy, and Sufficiency, which ensures
critical reasoning steps are preserved. Guided by these principles, we
introduce LC-R1, a post-training method based on Group Relative Policy
Optimization (GRPO). LC-R1 employs a novel combination of a Length Reward for
overall conciseness and a Compress Reward that is specifically designed to
remove the invalid portion of the thinking process. Extensive experiments on
multiple reasoning benchmarks demonstrate that LC-R1 achieves a significant
reduction in sequence length (~50%) with only a marginal (~2%) drop in
accuracy, achieving a favorable trade-off point on the Pareto frontier that
prioritizes high compression. Our analysis further validates the robustness of
LC-R1 and provides valuable insights for developing more powerful yet
computationally efficient LRMs. Our code is released at
https://github.com/zxiangx/LC-R1.