Deeper Medusa Smooth Operator 15022024 Link 〈Legit | Series〉

• Multiple Heads: Instead of one prediction head, Medusa adds several additional heads to the model. These heads predict the next several tokens in parallel.
• Tree-based Attention: It uses a specialized attention mechanism to process multiple candidate sequences simultaneously without increasing the computational cost significantly.
• Result: This allows the model to generate multiple tokens per forward pass, significantly reducing latency and increasing throughput (the "speedup").

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• The Challenge: Standard rejection sampling can be rigid and difficult to train because the acceptance criterion is discrete (a token is either accepted or rejected).
• The Solution: Recent works propose using temperature scaling or differentiable relaxation techniques. A "smooth" loss operator would allow gradients to flow more effectively through the acceptance mechanism, enabling the model to learn a better balance between diversity and accuracy in its predictions. This reduces the "variance" of the training process and leads to more stable convergence.

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