Paper page - Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention

Source: https://huggingface.co/papers/2605.22791

Abstract

Gated DeltaNet-2 improves upon existing linear attention models by separating erase and write operations through distinct channel-wise gates, achieving superior performance in long-context language modeling and retrieval tasks.

Linear attentionreplaces the unbounded cache ofsoftmax attentionwith a fixed-sizerecurrent state, reducing sequence mixing to linear time and decoding to constant memory. The hard part is not just what to forget, but how to edit this compressed memory without scrambling existing associations.Delta-rule modelssubtract the current read before writing a new value, andKimi Delta Attention(KDA) sharpens forgetting withchannel-wise decay. But the active edit still uses a single scalar gate to control two different things: how much old content to erase on the key side and how much new content to commit on the value side. We introduceGated DeltaNet-2, which generalizes bothGated DeltaNetand KDA by inheriting adaptive forgetting andchannel-wise decaywhile addressing their shared limitation, the scalar tie between erasing and writing. Gated Delta Rule-2 separates these roles with a channel-wiseerase gateb_t and a channel-wisewrite gatew_t, reducing to KDA when both gates collapse to the same scalar and toGated DeltaNetwhen the decay also collapses. We derive afast-weight updateview, achunkwise WY algorithmwithchannel-wise decayabsorbed into asymmetric erase factors, and agate-aware backward passthat preserves efficient parallel training. At 1.3B parameters trained on 100B FineWeb-Edu tokens,Gated DeltaNet-2 achieves the strongest overall results amongMamba-2,Gated DeltaNet, KDA, andMamba-3variants across language modeling, commonsense reasoning, and retrieval. Its advantage is most pronounced on long-contextRULERneedle-in-a-haystack benchmarks, where it improves the evaluated multi-key retrieval setting and remains strong in both recurrent and hybrid settings. Code is available at https://github.com/NVlabs/GatedDeltaNet-2.

View arXiv pageView PDFGitHub19Add to collection

Get this paper in your agent:

hf papers read 2605\.22791

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2605.22791 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2605.22791 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2605.22791 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.