LC-R1, a post-training method guided by Brevity and Sufficiency principles, reduces unnecessary reasoning in Large Reasoning Models with minimal accuracy loss.
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.