LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy
  Optimization
Authors: name: Xingyu Wu, name: Yuchen Yan, name: Shangke Lyu, name: Linjuan Wu, name: Yiwen Qiu, name: Yongliang Shen, name: Weiming Lu, name: Jian Shao, name: Jun Xiao, name: Yueting Zhuang

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.