Optimizing Length Compression in Large Reasoning Models
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.
685225250164cd13167104aa
Zhengxiang Cheng
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2025-06-18T16:15:41.166Z
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zxiang
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zx10086
685225250164cd13167104ab
Dongping Chen
685225250164cd13167104ac
Mingyang Fu
685225250164cd13167104ad
Tianyi Zhou
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2025-06-18T10:59:17.278Z
647f5af5b0e96764589f3b2a
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Tianyi Zhou
user
zhoutianyi
Optimizing Length Compression in Large Reasoning Models
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.
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 LRM post-training method Group Relative Policy Optimization GRPO Length Reward Compress Reward reasoning benchmarks Pareto frontier