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
Published: 2025-07-21 16:14:41

Abstract: 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.