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On-Policy Attention Linearization (OPAL) is introduced in which the hybrid attention student samples its own long-context trajectories and receives dense supervision from the frozen full-attention teacher.
Hierarchical Block Quantization (HBQ) uses large blocks to maximize efficiency and introduces low-overhead significand (SIG) scaling for second-level quantization and compensates for large-block errors more effectively than prior PoT and INT schemes.
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