Role-Decoupled Attention Residuals Enhance Transformer Depth Routing
A recent preprint on arXiv (2608.01075) presents Role-Decoupled Attention Residuals (RD-AttnRes), a slight enhancement to depth-routing residual frameworks within Transformers. These frameworks enable layers to access previous representations beyond just the last state. Current Block Attention Residuals rely on a single content-dependent depth mixture for queries, keys, and values, intertwining the distinct processes of matching (queries and keys) with content retrieval (values). RD-AttnRes separates these functions by employing a unified depth route for queries and keys, while allowing for an independent value route from the same residual sources. This approach requires only one additional model-width vector per layer and does not add extra token-to-token computations. The authors suggest that different depths may enhance matching and content retrieval. The paper can be found on arXiv with the identifier 2608.01075.
Key facts
- arXiv preprint 2608.01075 introduces Role-Decoupled Attention Residuals (RD-AttnRes).
- RD-AttnRes is a minimal extension to depth-routing residual architectures in Transformers.
- Existing Block Attention Residuals use a single content-dependent depth mixture for queries, keys, and values.
- RD-AttnRes shares one depth route between queries and keys while learning an independent value route.
- The extension adds only one model-width vector per layer.
- No additional token-to-token computation is introduced.
- The paper questions whether matching and content retrieval should read from the same depth.
- The preprint is announced as new on arXiv.
Entities
Institutions
- arXiv