Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse

Meng Li; Runsheng Wang; Wenxuan Zeng; Zizhuo Fu

arxiv: 2602.01203 · v3 · pith:UX46GXRWnew · submitted 2026-02-01 · 💻 cs.CL · cs.LG

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse

Zizhuo Fu , Wenxuan Zeng , Runsheng Wang , Meng Li This is my paper

Pith reviewed 2026-05-16 08:44 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords attention sinkmixture of expertshead collapselarge language modelssink-aware trainingattention layersload balancing

0 comments

The pith

Attention sinks in transformers naturally build a Mixture-of-Experts structure inside attention layers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that the attention sink, where models disproportionately focus on the first token, functions as an implicit router that turns attention heads into experts in a Mixture-of-Experts setup. This routing creates load imbalance, which directly accounts for head collapse where only a small fixed group of heads drives generation. The authors introduce sink-aware training that adds an auxiliary load-balancing loss to attention layers, restoring balanced head usage. Experiments confirm gains across vanilla attention, sink attention, and gated attention. Readers should care because the insight reframes a known inefficiency as a trainable feature rather than an unavoidable artifact.

Core claim

The sink in Vanilla Attention and Sink Attention naturally constructs a Mixture-of-Experts (MoE) mechanism within attention layers. This insight explains the head collapse phenomenon observed in prior work, where only a fixed subset of attention heads contributes to generation. To mitigate head collapse, the authors propose a sink-aware training algorithm with an auxiliary load balancing loss designed for attention layers that achieves effective head load balancing and improves model performance across Vanilla Attention, Sink Attention, and Gated Attention.

What carries the argument

The attention sink serving as a natural router that assigns tokens unevenly to attention heads, thereby forming an implicit MoE structure whose load imbalance produces head collapse.

If this is right

Adding the auxiliary load-balancing loss during training restores contribution from more heads in vanilla, sink, and gated attention variants.
Balanced head usage produces measurable gains in downstream model performance.
Attention layers can be treated as native expert systems whose routing is controlled by the sink token.
The same balancing technique applies uniformly to multiple attention formulations without changing their core architecture.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Explicit MoE layers might be merged with attention sinks to create hybrid architectures that control routing more precisely.
Inference cost could drop if balanced heads reduce the need to run all heads at full capacity.
The same sink-based routing view may apply to other sequence models that exhibit first-token dominance.

Load-bearing premise

That the attention sink directly creates an MoE routing mechanism and that its resulting load imbalance is the main driver of head collapse.

What would settle it

Training or evaluating a model in which the first-token attention weight is forced to a uniform distribution and then checking whether head collapse still occurs.

Figures

Figures reproduced from arXiv: 2602.01203 by Meng Li, Runsheng Wang, Wenxuan Zeng, Zizhuo Fu.

**Figure 1.** Figure 1: Vanilla Attention used in most open-source models. Sink Attention used in GPT-OSS with a learnable bias sink added to the softmax denominator. Gated Attention used in Qwen3-Next with a head-wise gating factor computed via sigmoid activation. To address the collapse issue and improve head load balancing, we propose a sink-aware auxiliary load balancing loss for all three attention mechanisms. It leverages … view at source ↗

**Figure 2.** Figure 2: ℓ2-norm of token values across models. The first token’s value vector approaches zero in models with Vanilla Attention. effective output becomes: O l,h t = X j̸=sink A l,h t,j · v l,h j = (1 − A l,h t,sink)· X j̸=sink A˜l,h t,j · v l,h j (3) where A˜l,h t,j denotes the re-normalized softmax attention weights over non-sink tokens, defined as A˜l,h t,j = A l,h t,j /(1 − A l,h t,sink) for j ̸= sink. The term… view at source ↗

**Figure 3.** Figure 3: Visualization of attention patterns and query-key geometry. Each panel shows the attention map (left) and PCA projection of query and key vectors (right), where red stars indicate k0. (a)(b)(c) Vanilla Attention models exhibit attention sink and constrained query-key geometry. (d)(e)(f) Sink Attention and Gated Attention models show no sink phenomenon and more flexible vector distributions. 3.2. Advantages… view at source ↗

**Figure 4.** Figure 4: Head importance scores over all tokens and samples for LLaMA-3.1-8B (Vanilla Attention), GPT-OSS-20B (Sink Attention), and Qwen3-Next-80B-A3B (Gated Attention). Qwen3-Next80B-A3B uses different numbers of heads in different layers. Quantifying Head Importance. Building on our analysis in Section 3.1 that attention layers exhibit a native MoE structure, we can now examine head collapse through the lens of… view at source ↗

**Figure 5.** Figure 5: Head load imbalance during training across model scales and attention mechanisms. Each subplot shows the coefficient of variation (Equation (6)) over training steps for models trained with and without the auxiliary load balancing loss. Raw data and exponential moving average (EMA) smoothed trends are displayed. fused operations, which significantly degrade training and inference efficiency. We propose an … view at source ↗

**Figure 6.** Figure 6: Head load activation across different datasets for 2B models trained with and without the auxiliary load balancing loss. Sink Attention and Gated Attention consistently outperform Vanilla Attention across all model sizes. This improvement aligns with our analysis in Section 3.2, demonstrating that eliminating attention sink enhances model expressiveness not only on long-context tasks but also on general ta… view at source ↗

**Figure 7.** Figure 7: Model accuracy across datasets over training steps for different attention mechanisms. Vanilla Attention Sink Attention Gated Attention Training Steps Training Steps Training Steps Training Loss Training Loss Val BPB Val BPB 0.6B 2B 3.0 2.9 2.8 2.6 2.5 8.7 8.5 8.3 8.1 7.6 7.4 7.2 7.0 1e-1 1e-1 50k 60k 70k 50k 60k 70k 50k 60k 70k 12k 16k 20k 12k 16k 20k 12k 16k 20k [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Training loss and validation BPB during training across model scales and attention mechanisms. Each subplot compares models trained with and without the auxiliary load balancing loss. stabilizes at a plateau. In contrast, Vanilla Attention and Sink Attention display a gradual increase in imbalance over training steps. This difference arises from the nature of their gating mechanisms. In Gated Attention, th… view at source ↗

**Figure 9.** Figure 9: Head importance scores across the GPT-OSS, LLaMA-2, and LLaMA-3 families. the imbalance becomes more severe as model size increases. Larger models exhibit a more extreme disparity between active and dormant heads, suggesting that head collapse intensifies with scale. Results on Qwen Family [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗

**Figure 10.** Figure 10: Head importance scores across the Qwen2.5 and Qwen3 families. Larger models demonstrate more severe head collapse with lower overall head utilization. Key Observations. The results across all three model families reveal a clear trend: head collapse intensifies with model scale. Models with more parameters and more attention heads paradoxically exhibit lower head utilization rates. This suggests that simpl… view at source ↗

**Figure 11.** Figure 11: Head importance scores Impl,h for Qwen3-8B across three conditions: before fine-tuning (left), after one epoch of fine-tuning with Equation (7) (middle), and after one epoch of fine-tuning with Equation (8) (right). These heads carry critical learned representations, and reducing their influence causes performance degradation. Consequently, their gating factors resist change during fine-tuning. When the … view at source ↗

read the original abstract

Large Language Models (LLMs) often assign disproportionate attention to the first token, a phenomenon known as the attention sink. Several recent approaches aim to address this issue, including Sink Attention in GPT-OSS and Gated Attention in Qwen3-Next. However, a comprehensive analysis of the relationship among these attention mechanisms is lacking. In this work, we provide both theoretical and empirical evidence demonstrating that the sink in Vanilla Attention and Sink Attention naturally construct a Mixture-of-Experts (MoE) mechanism within attention layers. This insight explains the head collapse phenomenon observed in prior work, where only a fixed subset of attention heads contributes to generation. To mitigate head collapse, we propose a sink-aware training algorithm with an auxiliary load balancing loss designed for attention layers. Extensive experiments show that our method achieves effective head load balancing and improves model performance across Vanilla Attention, Sink Attention, and Gated Attention. We hope this study offers a new perspective on attention mechanisms and encourages further exploration of the inherent MoE structure within attention layers.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper links attention sinks to a native MoE structure that explains head collapse and adds a balancing loss that helps across attention variants, but the MoE claim rests on a static position bias rather than input-dependent routing.

read the letter

The core contribution is a unified story that treats the attention sink as the mechanism that turns standard attention into something like a mixture of experts, where a few heads dominate and the rest collapse. They support this with both a theoretical framing and experiments showing the pattern holds in vanilla attention, sink attention, and gated attention. The practical piece is a sink-aware auxiliary loss that balances head usage and lifts performance without changing the base architecture much. That part looks useful on its own; the loss is simple to add and the gains appear consistent across the three attention styles they test. What the work does cleanly is connect two existing observations—sink behavior and head collapse—into one causal account and then give a direct fix. The experiments are the strongest part because they show the balancing actually moves the needle on downstream metrics. The softer spot is the MoE analogy itself. The sink is locked to the first token position, so any routing is position-static rather than conditioned on token content or input features. Standard MoE routers make expert choice depend on the current input; here the selection is largely fixed by position, which makes the load-imbalance explanation correlational rather than a demonstration of dynamic expert routing. The auxiliary loss may simply be regularizing that fixed bias rather than restoring true MoE dynamics. If the paper shows head specialization varying meaningfully with input semantics, that would tighten the claim; from the abstract and stress-test note it is not yet clear that step is there. This is worth a serious referee for groups working on attention internals or training tricks for long-context models. The idea is coherent enough and the empirical patch is concrete, even if the theoretical framing needs more scrutiny on what counts as native MoE. I would bring it to a reading group for the discussion on whether the analogy holds.

Referee Report

3 major / 2 minor

Summary. The paper claims that the attention sink in vanilla attention and sink attention naturally constructs a Mixture-of-Experts (MoE) mechanism within attention layers, where the first-token bias routes computation such that only a subset of heads act as active experts; this explains the head collapse phenomenon. It introduces a sink-aware training algorithm with an auxiliary load-balancing loss to restore balanced head utilization and reports performance gains across vanilla, sink, and gated attention variants.

Significance. If the central interpretation holds, the work offers a unifying view of attention as an implicit MoE structure, which could explain scaling behaviors and head specialization in LLMs while providing a practical training fix for collapse. The cross-variant experiments and auxiliary loss are concrete contributions that could influence attention design.

major comments (3)

[§3.1] §3.1 (Theoretical Analysis): The claim that the sink 'naturally constructs' an MoE requires showing input-dependent routing. The sink is a fixed first-position bias; the derivation must demonstrate that non-sink attention weights exhibit high mutual information with token embeddings or that head specialization varies conditionally across inputs, rather than being position-static.
[§4.2, Eq. (8)] §4.2, Eq. (8): The auxiliary load-balancing loss is introduced to address MoE imbalance, yet its weighting appears tuned to observed head statistics. An ablation isolating the loss from other training changes, or comparison to standard MoE balancing objectives, is needed to confirm it restores native MoE dynamics rather than applying generic regularization.
[Table 2] Table 2 (Head utilization metrics): The reported correlation between sink strength and collapse is clear, but without a causal test (e.g., sink removal or forced uniform attention and measurement of collapse reversal), the explanation that the sink forges the MoE structure remains associative.

minor comments (2)

[§2] §2 (Related Work): Additional citations to recent attention-sink analyses (post-2023) would better situate the MoE reinterpretation.
[Figure 3] Figure 3: The head-contribution heatmaps would benefit from error bars or multiple random seeds to indicate statistical reliability of the observed specialization patterns.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of our theoretical and empirical claims. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [§3.1] §3.1 (Theoretical Analysis): The claim that the sink 'naturally constructs' an MoE requires showing input-dependent routing. The sink is a fixed first-position bias; the derivation must demonstrate that non-sink attention weights exhibit high mutual information with token embeddings or that head specialization varies conditionally across inputs, rather than being position-static.

Authors: We thank the referee for this precise observation. In §3.1 our derivation establishes that the fixed sink bias partitions attention capacity such that the remaining heads function as specialized experts whose activation depends on query-key similarities with subsequent tokens. While the sink position itself is fixed, the non-sink attention weights are computed via input-dependent softmax operations. To address the request for explicit input-dependence, we will augment §3.1 with mutual-information measurements between non-sink attention distributions and token embeddings across diverse inputs, confirming conditional head specialization. revision: yes
Referee: [§4.2, Eq. (8)] §4.2, Eq. (8): The auxiliary load-balancing loss is introduced to address MoE imbalance, yet its weighting appears tuned to observed head statistics. An ablation isolating the loss from other training changes, or comparison to standard MoE balancing objectives, is needed to confirm it restores native MoE dynamics rather than applying generic regularization.

Authors: We agree that isolating the contribution of the auxiliary loss is necessary. The coefficient in Eq. (8) was selected via preliminary runs to achieve load balance without harming perplexity. In the revision we will add a controlled ablation that trains identical models with and without the load-balancing term while freezing all other hyperparameters. We will also report a direct comparison against the standard MoE balancing loss from Switch Transformer to demonstrate that our formulation specifically counters attention-sink-induced imbalance. revision: yes
Referee: [Table 2] Table 2 (Head utilization metrics): The reported correlation between sink strength and collapse is clear, but without a causal test (e.g., sink removal or forced uniform attention and measurement of collapse reversal), the explanation that the sink forges the MoE structure remains associative.

Authors: The referee correctly identifies that our current evidence is largely correlational. Although experiments across vanilla, sink, and gated attention already demonstrate that modifying the sink alters head utilization, we acknowledge the value of a direct causal intervention. We will add a new experiment that applies a regularization term to enforce uniform attention on the first token and quantifies the resulting reversal in head-collapse metrics; the results will be included in an expanded Table 2 and accompanying analysis. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper claims to demonstrate via theoretical and empirical evidence that the attention sink naturally constructs an MoE mechanism within attention layers, using this to explain head collapse and motivate a new auxiliary load-balancing loss. No equations or definitions are shown that reduce the MoE routing to a direct redefinition of the sink (or vice versa), nor is any 'prediction' shown to be a fitted parameter renamed as output. The auxiliary loss is presented as an independent addition rather than a statistical necessity of prior fits. The derivation chain therefore remains self-contained against external benchmarks of attention behavior and does not collapse to self-citation or construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on the domain assumption that sink behavior equates to MoE routing; no new free parameters or invented entities are introduced beyond the auxiliary loss coefficient whose value is not specified in the abstract.

axioms (1)

domain assumption Attention sink naturally constructs an MoE mechanism within attention layers
Invoked as the load-bearing theoretical finding that explains head collapse.

pith-pipeline@v0.9.0 · 5487 in / 1177 out tokens · 40960 ms · 2026-05-16T08:44:55.427129+00:00 · methodology

discussion (0)

Forward citations

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