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Hybrid attention models have emerged as a crucial architecture for Large Language Models (LLMs) (e.g., the Qwen3.5 and Kimi series). Their memory and computational efficiency make them highly attractive for on-device inference, forming a promising synergy with edge Neural Processing Units (NPUs). However, naive executi...
Efficient Long-hOrizon (ELO) learning is introduced, an efficient meta-training algorithm that reallocates wasted meta-training compute to longer failure regimes, achieving efficient long-horizon learning, and enforces decoupled progressive expert supervision, providing stable meta-learning signals that additionally im...
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