Summary
Diffusion Large Language Models (dLLMs) offer benefits like parallel decoding but currently require billions of parameters for competitive performance. Existing distillation methods reduce inference steps within a single architecture, failing to address knowledge transfer between models with different architectures. TIDE introduces the first framework for cross-architecture dLLM distillation, enabling efficient knowledge transfer even when teacher and student models vary in architecture, attention mechanisms, and tokenizers.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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