LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908489291399168 |
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| author | Wu, Xingyu Yan, Yuchen Lyu, Shangke Wu, Linjuan Qiu, Yiwen Shen, Yongliang Lu, Weiming Shao, Jian Xiao, Jun Zhuang, Yueting |
| author_facet | Wu, Xingyu Yan, Yuchen Lyu, Shangke Wu, Linjuan Qiu, Yiwen Shen, Yongliang Lu, Weiming Shao, Jian Xiao, Jun Zhuang, Yueting |
| contents | Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for simple problems. We present Length-Adaptive Policy Optimization (LAPO), a novel framework that transforms reasoning length control from an external constraint into an intrinsic model capability. Unlike existing approaches that impose rigid limits or rely on post-hoc interventions, LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process. In the first stage, models learn natural reasoning patterns by discovering the statistical distribution of successful solution lengths. The second stage leverages these patterns as meta-cognitive guidance, embedding them directly within the model's reasoning context to ensure inference-time flexibility. Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9% while improving accuracy by 2.3%. Our analysis reveals that models trained with LAPO develop emergent abilities to allocate computational resources based on problem complexity, achieving efficient reasoning without sacrificing quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization Wu, Xingyu Yan, Yuchen Lyu, Shangke Wu, Linjuan Qiu, Yiwen Shen, Yongliang Lu, Weiming Shao, Jian Xiao, Jun Zhuang, Yueting Artificial Intelligence Computation and Language Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for simple problems. We present Length-Adaptive Policy Optimization (LAPO), a novel framework that transforms reasoning length control from an external constraint into an intrinsic model capability. Unlike existing approaches that impose rigid limits or rely on post-hoc interventions, LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process. In the first stage, models learn natural reasoning patterns by discovering the statistical distribution of successful solution lengths. The second stage leverages these patterns as meta-cognitive guidance, embedding them directly within the model's reasoning context to ensure inference-time flexibility. Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9% while improving accuracy by 2.3%. Our analysis reveals that models trained with LAPO develop emergent abilities to allocate computational resources based on problem complexity, achieving efficient reasoning without sacrificing quality. |
| title | LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2507.15758 |