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![AINativeF Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::1795402815298486272.png) AI Native Foundation [@AINativeF](/creator/twitter/AINativeF) on x 2110 followers
Created: 2025-07-26 00:51:21 UTC

X. LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization

🔑 Keywords: Length-Adaptive Policy Optimization, Reinforcement Learning, Reasoning Models, Mathematical Reasoning

💡 Category: Reinforcement Learning

🌟 Research Objective:
   - Introduce Length-Adaptive Policy Optimization (LAPO) to transform reasoning length control into an intrinsic model capability.

🛠️ Research Methods:
   - Use a two-stage reinforcement learning process to teach models natural reasoning patterns and meta-cognitive guidance for efficient reasoning.

💬 Research Conclusions:
   - LAPO reduces token usage by up to XXXX% and improves accuracy by 2.3%, with models developing the ability to allocate computational resources effectively.

👉 Paper link:

![](https://pbs.twimg.com/media/GwvqYRsakAAY4a8.jpg)

XX engagements

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**Related Topics**
[lapo](/topic/lapo)
[coins ai](/topic/coins-ai)

[Post Link](https://x.com/AINativeF/status/1948908968723841085)