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README.md
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### Key Features
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- Training-Inference Collaboration: apply Muon optimizer with dense MTP, develop novel optimization techniques to resolve instabilities while scaling up, delivering 1.3× to 1.7× the throughput of the non-MTP version.
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- Agentic Intelligence: designed for tool use, reasoning, and autonomous problem-solving.
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### Key Features
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- Fibration Policy Optimization: Introduces fiber bundle theory into reinforcement learning, proposing a novel optimization framework, FiberPO. This method is specifically designed to handle the challenges of large-scale and heterogeneous agent training, improving stability and robustness under complex data distributions. [paper link](https://arxiv.org/abs/2603.08239)
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- Training-Inference Collaboration: apply Muon optimizer with dense MTP, develop novel optimization techniques to resolve instabilities while scaling up, delivering 1.3× to 1.7× the throughput of the non-MTP version.
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- Agentic Intelligence: designed for tool use, reasoning, and autonomous problem-solving.
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