Adaptive Computation Depth via Learned Token Routing in Transformers
📰 ArXiv cs.AI
Learn to adapt computation depth in transformers using Token-Selective Attention (TSA) for efficient processing
Action Steps
- Implement Token-Selective Attention (TSA) in a transformer model using a lightweight two-layer MLP
- Train the TSA module end-to-end with the transformer model to learn halting probabilities
- Apply TSA to route tokens through the transformer layers based on contextual difficulty
- Evaluate the performance of the TSA-enabled transformer model on a benchmark dataset
- Compare the computational efficiency of the TSA-enabled model with a standard transformer model
Who Needs to Know This
ML researchers and engineers can benefit from this technique to optimize transformer models for various NLP tasks, reducing computational costs and improving performance
Key Insight
💡 Adaptive computation depth via learned token routing can improve transformer efficiency without sacrificing performance
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🤖 Learn to adapt computation depth in transformers with Token-Selective Attention (TSA) and reduce computational costs! #transformers #NLP
Key Takeaways
Learn to adapt computation depth in transformers using Token-Selective Attention (TSA) for efficient processing
Full Article
Title: Adaptive Computation Depth via Learned Token Routing in Transformers
Abstract:
arXiv:2605.05222v1 Announce Type: cross Abstract: Standard transformer architectures apply the same number of layers to every token regardless of contextual difficulty. We present Token-Selective Attention (TSA), a learned per-token gate on residual updates between consecutive transformer blocks. Each gate is a lightweight two-layer multi-layer perceptron (MLP) that produces a continuous halting probability, making the mechanism end-to-end differentiable with 1.7% parameter overhead and no chang
Abstract:
arXiv:2605.05222v1 Announce Type: cross Abstract: Standard transformer architectures apply the same number of layers to every token regardless of contextual difficulty. We present Token-Selective Attention (TSA), a learned per-token gate on residual updates between consecutive transformer blocks. Each gate is a lightweight two-layer multi-layer perceptron (MLP) that produces a continuous halting probability, making the mechanism end-to-end differentiable with 1.7% parameter overhead and no chang
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