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Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning
Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. Structured pruning reduces these costs by removing architectural components, yet its recovery stage is often limited by a mismatch between the recovery
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