Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformers
📰 ArXiv cs.AI
Learn how attention sinks and massive activations regulate gradients in Transformers, crucial for stable training and improved performance
Action Steps
- Investigate the role of attention sinks in pre-norm Transformers using backpropagation
- Analyze the impact of massive activations on gradient flows in Transformer sublayers
- Apply gradient regulation techniques to stabilize training and improve model performance
- Compare the effects of different normalization schemes on attention sinks and gradient regulation
- Implement and test Transformer models with modified attention mechanisms to mitigate gradient sinks
Who Needs to Know This
ML researchers and engineers working with Transformers will benefit from understanding the relationship between attention sinks, massive activations, and gradient regulation, enabling them to design more efficient and effective models
Key Insight
💡 Attention sinks and massive activations play a crucial role in regulating gradients in Transformers, and understanding this relationship is key to designing more efficient and effective models
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🤖 Attention sinks induce gradient sinks in Transformers! 📈 New research reveals the importance of massive activations in regulating gradients 📊 #Transformer #GradientRegulation #ML
Key Takeaways
Learn how attention sinks and massive activations regulate gradients in Transformers, crucial for stable training and improved performance
Full Article
Title: Attention Sinks Induce Gradient Sinks: Massive Activations as Gradient Regulators in Transformers
Abstract:
arXiv:2603.17771v2 Announce Type: replace-cross Abstract: Attention sinks and massive activations are recurring and closely related phenomena in Transformer models. Existing explanations have largely focused on the forward pass, yet in pre-norm Transformers, large residual-stream norms play only an indirect forward role because sublayers operate on normalized inputs. We study this relationship from the perspective of backpropagation. Empirically and theoretically, we show that under causal maski
Abstract:
arXiv:2603.17771v2 Announce Type: replace-cross Abstract: Attention sinks and massive activations are recurring and closely related phenomena in Transformer models. Existing explanations have largely focused on the forward pass, yet in pre-norm Transformers, large residual-stream norms play only an indirect forward role because sublayers operate on normalized inputs. We study this relationship from the perspective of backpropagation. Empirically and theoretically, we show that under causal maski
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