Token-Level Sparsity in Neural Models

Updated 11 January 2026

Token-level sparsity is a strategy that selectively retains or prunes individual tokens and features in transformer models to improve efficiency.
It employs methods like element-wise pruning, adaptive token selection, and expert routing to balance compute reduction with minimal accuracy loss.
Empirical studies demonstrate significant improvements in memory usage, FLOPs reduction, and inference speed, enabling scalable on-device deployment.

Token-level sparsity refers to the selective activation or retention of information at the granularity of individual tokens within neural models, most notably in transformer-based architectures. Instead of dense computation over all tokens and all corresponding activations, token-level sparsity paradigms dynamically or statically decide which tokens (or their features) are necessary for downstream computation, trading off compute, memory, and inference cost against accuracy retention. This approach encompasses unstructured element-wise pruning, expert routing, adaptive token pruning, and sparsity-aware training methods, and is now widely used to enable tractable scaling, on-device inference, and systematic efficiency improvements in LLMs, vision-LLMs (VLMs), multimodal transformers, and diffusion architectures.

1. Definitions and Formalization

Token-level sparsity is fundamentally characterized by reducing the fraction of tokens or token-to-feature interactions processed per forward or backward pass. Formally, given an input sequence of $T$ tokens and an internal hidden dimension %%%%1%%%%, token-level sparsity can target:

Element-wise vector sparsity: For each token $t$ , a mask $m_t \in \{0, 1\}^d$ indicates which feature dimensions are retained (e.g., per-token pruning of KV cache rows as in Mustafar (Joo et al., 28 May 2025)).
Token selection/pruning: A binary mask $g_j \in\{0,1\}$ over tokens indicates retention or skipping, imposed per layer/block (e.g., FTP (Li et al., 2024), LeMo (Wang et al., 15 Jan 2025)).
Expert activation sparsity: In MoE architectures, token $t$ activates only a subset of $N$ experts, with average token-level sparsity defined as $TLS = 1 - \mathbb{E}_t [|S(t)|/N]$ (see BlockFFN (Song et al., 11 Jul 2025)).
Activation sparsity: Fraction of nonzero activations per token, often in FFN layers (Spark (You et al., 7 Jun 2025), ReLU transformers (Wild et al., 2024)).
Visual token sparsity: In VLMs, the proportion of image/video tokens entering attention, scored by cross-modal interaction (SparseVLM (Zhang et al., 2024), VLM-Pruner (Wu et al., 2 Dec 2025)).

Sparsity levels $s \in [0,1]$ or keep ratios $\rho = 1-s$ can be set globally, per layer, or adaptively per token via predictors or learned routers.

2. Pruning and Routing Methodologies

Approaches to induce and control token-level sparsity encompass both static heuristics and adaptive, trainable strategies:

Element-wise Per-token Pruning

For autoregressive KV caches, element-wise magnitude pruning is applied per token:

For $K_t \in \mathbb{R}^d$ , define threshold $\tau_k$ as the $k$ -th largest $|K_t|$ value ( $k = \lfloor (1-s)d \rfloor$ ).
Mask: $m_{t,i} = 1$ if $|K_{t,i}| \ge \tau_k$ , else 0.
Pruned vector: $K_t^{\text{pruned}} = K_t \odot m_t$ ensuring $\|K_t^{\text{pruned}}\|_0 = (1-s) d$ (Joo et al., 28 May 2025).

Token-Wise Routing

Trainable routers (MLPs, Gumbel softmax, genetic algorithms) predict binary gates for skip/execute decisions per token:

Routers consume low-dimensional inputs (token position, attention score, rank, block sparsity) and minimize loss terms for knowledge distillation, sparsity constraint, and router agreement (Li et al., 2024).
Informed routing uses feature forecasters to approximate unit outputs and ranks tokens by recoverability (Han et al., 10 Oct 2025).

Expert Routing in MoE

TopK gating activates $K$ experts per token; SeqTopK reallocates the expert budget over the full sequence, enabling easy tokens to get fewer experts and complex ones more, respecting $\sum_{t,i} R_{t,i} = T\times K$ (Wen et al., 9 Nov 2025).

Block-Wise and Segment Sparsification

Grouping tokens into blocks aligns with hardware, collects interaction statistics, and prunes whole blocks based on informativeness measures, using thresholds learned per layer (Wang et al., 15 Jan 2025). Segment-based peak cutting further reduces activation spikes.

RL-driven Sparsity

Policy gradient methods use efficiency reward ($1 -$ token ratio) and performance reward to post-train MLLMs to minimize active token count at constant accuracy (Chen et al., 23 Apr 2025).

Statistical Top-K and Approximate Sorting

Statistical algorithms estimate a threshold based on mean and variance (Gaussian quantile), selecting top- $k$ entries with $O(d)$ cost, mitigating hardware bottlenecks (You et al., 7 Jun 2025).

3. Sparse Representation and Acceleration Kernels

Efficient support for token-level sparsity demands specialized data structures and compute kernels:

Bitmap-based formats: Compress pruned vectors into blocks, storing bitmaps and contiguous nonzeros with tile-offsets; enables direct computation over compressed buffers in attention SpMV (Joo et al., 28 May 2025).
Sparse attention kernels: Rewrite attention as sparse matrix–dense vector mults (SpMV), with decompression-load-accumulate and dense local ops (Joo et al., 28 May 2025, You et al., 7 Jun 2025).
Hardware-friendly routing: Token-wise and chunk-wise custom CUDA kernels fuse sparsity with speculative decoding, maximizing reuse and achieving up to 3.7× speedup (Song et al., 11 Jul 2025).

4. Impact on Efficiency, Quality, and Scaling

Empirical studies consistently demonstrate substantial gains:

Method	Model/Task	Sparsity Level	Memory/FLOPs Reduction	Speedup	Accuracy Impact
Mustafar (Joo et al., 28 May 2025)	Llama-3-8B, decode	70%	~2.2× (KV cache)	2.23×	<2 points (QA)
LeMo (Wang et al., 15 Jan 2025)	Llama2-7B, fine-tune	~50%	1.93× (memory)	1.36×	<2% loss
FTP (Li et al., 2024)	LLaMA2-7B, ARC/MMUL	22–40%	~1.28–1.61×	98–100% retention
BlockFFN (Song et al., 11 Jul 2025)	MoE (XLarge)	>80% TLS	3.7× (token-wise)	70% CLS_8	No PPL/acc loss
ZipR1 (Chen et al., 23 Apr 2025)	Qwen2VL-7B	~23% tokens	~4×	0.5 point (MMBench)
Spark (You et al., 7 Jun 2025)	Gemma-2 recipe	8% FFN active	2.5× FLOPs	1.79× CPU	<0.2% accuracy loss

Token-level sparsity is confirmed to enable longer contexts, greater throughput (tokens/sec), and memory scaling on commodity GPUs. Quality drops are generally negligible up to aggressive pruning rates (0.7 or higher).

5. Layer-Dependent and Modality-Adaptive Sparsity

Layerwise sparsity evolves systematically, reflecting model internals:

ReLU Transformers: Early layers retain higher per-token activations/ ${U_{\text{token}}}$ but low diversity; upper layers shift to highly selective "binary-like" features, maximizing batch coverage/ ${U_{\text{batch}}}$ (Wild et al., 2024).
Multimodal/VLM pruning: Visual tokens scored by cross-attention to language tokens, adapted per layer by rank and density clustering; spatial sparsity enforced via centrifugal BSS criteria (Wu et al., 2 Dec 2025, Zhang et al., 2024).
Sparse autoencoders: Token-level sparsity hyperparameter $L_0$ is critical; mis-specification mixes features and degrades interpretability. The optimal $L_0$ is detectible via decoder projection scores and probing F1 (Chanin et al., 22 Aug 2025).

6. Specialized Applications and Limitations

The token-level sparsity paradigm is not restricted to LLMs:

Diffusion models: Drops a fraction $\gamma$ of tokens per layer (masking or routing). Sparse Guidance leverages differing sparsity rates for high-variance or low-variance conditional prediction, yielding state-of-the-art FID with up to 58% FLOP savings (Krause et al., 4 Jan 2026).
Spiking Transformers: Token keep/drop decisions made by spike firing rate; MLP gating and Gumbel-Softmax select important foreground tokens, leading to up to 26% GFLOPs reduction with minimal accuracy loss (Liu et al., 2023).

Limitations noted include the need for careful hyperparameter tuning (e.g., pruning schedules, router thresholds), potential additional post-training cost, and, for multimodal settings, dependencies on strong cross-modal alignment.

7. Future Directions and Open Questions

Emerging areas and proposals include:

Joint sparsity scheduling: Per-layer and per-head dynamic adaptation beyond global uniform rates (Joo et al., 28 May 2025).
RL-driven sparsity in language-only long-context models (Chen et al., 23 Apr 2025).
Token-sparse training for video, audio, and cross-modal architectures (Krause et al., 4 Jan 2026).
Low-bit quantized sparse representations and bitmap formats (Joo et al., 28 May 2025).
Sparse-aware offload and 2D-sparsity schemes for further scaling (Wang et al., 15 Jan 2025).
Sparsity-aligned allocation of layer widths to maximize utilization (Wild et al., 2024).

A plausible implication is that token-level sparsity, carefully scheduled and adaptively routed, will remain central for efficient large-model training, inference, and deployment, spanning language, vision, and generative paradigms.

References

Representative technical details and empirical results are drawn from: Mustafar (Joo et al., 28 May 2025), SeqTopK (Wen et al., 9 Nov 2025), ZipR1 (Chen et al., 23 Apr 2025), LeMo (Wang et al., 15 Jan 2025), Spark Transformer (You et al., 7 Jun 2025), VLM-Pruner (Wu et al., 2 Dec 2025), SparseVLM (Zhang et al., 2024), ReLU Transformers (Wild et al., 2024), TEPO (Lin et al., 10 Oct 2025), Informed Routing (Han et al., 10 Oct 2025), FTP (Li et al., 2024), BlockFFN (Song et al., 11 Jul 2025), Sparse Guidance (Krause et al., 4 Jan 2026), SparseSpikformer (Liu et al., 2023), and Sparse SAE (Chanin et al., 22 Aug 2025).