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

Hugging Face Daily Papers Papers
linear-attention delta-rule gated-delta-net-2 long-context language-modeling retrieval efficient-attention

Summary

Gated DeltaNet-2 introduces separate erase and write gates for linear attention, achieving superior performance in long-context language modeling and retrieval tasks.

Linear attention replaces the unbounded cache of softmax attention with a fixed-size recurrent 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 models subtract the current read before writing a new value, and Kimi Delta Attention (KDA) sharpens forgetting with channel-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 introduce Gated DeltaNet-2, which generalizes both Gated DeltaNet and KDA by inheriting adaptive forgetting and channel-wise decay while addressing their shared limitation, the scalar tie between erasing and writing. Gated Delta Rule-2 separates these roles with a channel-wise erase gate b_t and a channel-wise write gate w_t, reducing to KDA when both gates collapse to the same scalar and to Gated DeltaNet when the decay also collapses. We derive a fast-weight update view, a chunkwise WY algorithm with channel-wise decay absorbed into asymmetric erase factors, and a gate-aware backward pass that preserves efficient parallel training. At 1.3B parameters trained on 100B FineWeb-Edu tokens, Gated DeltaNet-2 achieves the strongest overall results among Mamba-2, Gated DeltaNet, KDA, and Mamba-3 variants across language modeling, commonsense reasoning, and retrieval. Its advantage is most pronounced on long-context RULER needle-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.
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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.

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