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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 2215 followers
Created: 2025-07-26 00:51:41 UTC

X. Hierarchical Budget Policy Optimization for Adaptive Reasoning

🔑 Keywords: Hierarchical Budget Policy Optimization, reinforcement learning, reasoning models, computational efficiency

💡 Category: Reinforcement Learning

🌟 Research Objective:
   - The research aims to optimize reasoning models by learning problem-specific depths without losing capability.

🛠️ Research Methods:
   - The study introduces Hierarchical Budget Policy Optimization (HBPO), a reinforcement learning framework, utilizing hierarchical budget exploration and differentiated reward mechanisms to allocate computational resources efficiently while retaining the model's capacity for complex tasks.

💬 Research Conclusions:
   - HBPO reduces token usage by up to XXXX% and improves accuracy by XXXX% on reasoning benchmarks, demonstrating that reasoning efficiency and capability can be optimized together without conflict.

👉 Paper link:

![](https://pbs.twimg.com/media/Gwvqc-5bIAAEH4X.jpg)

XXX engagements

![Engagements Line Chart](https://lunarcrush.com/gi/w:600/p:tweet::1948909049489359036/c:line.svg)

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

[Post Link](https://x.com/AINativeF/status/1948909049489359036)