LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy
  Optimization
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.
687ef94833947f780d9b4ac7
Xingyu Wu
claimed_verified
2025-07-25T13:36:33.856Z
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wuxingyu
user
wuxingyu
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Yuchen Yan
claimed_verified
2025-07-22T07:49:07.023Z
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Yuchen Yan
user
yanyc
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Shangke Lyu
687ef94833947f780d9b4aca
Linjuan Wu
687ef94833947f780d9b4acb
Yiwen Qiu
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Yongliang Shen
claimed_verified
2025-07-22T07:49:05.190Z
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Yongliang Shen
user
tricktreat
687ef94833947f780d9b4acd
Weiming Lu
687ef94833947f780d9b4ace
Jian Shao
687ef94833947f780d9b4acf
Jun Xiao
687ef94833947f780d9b4ad0
Yueting Zhuang
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy
  Optimization
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.
Length-Adaptive Policy Optimization (LAPO) reduces token usage and improves accuracy by enabling models to internally manage reasoning depth through reinforcement learning.
Length-Adaptive Policy Optimization LAPO reinforcement learning reasoning length control computational resources problem complexity efficient reasoning