Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models

2026-07-13 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new approach to efficient Large Language Model (LLM) inference, combining Sensitivity-Aware Thresholding for Sparsity (SATS) and token-level conditional routing, has been introduced. SATS is a threshold calibration method that selects layerwise gate thresholds based on a local MLP output sensitivity proxy, rather than traditional percentile-based calibration. This method preserves the existing mechanism of sparsifying MLP activations. Complementing SATS, a lightweight token routing framework dynamically chooses between a dense base path and a sparse path for individual tokens, using precomputed token identity-based damage estimates. Evaluated on llama 3.1 8B and Qwen 3 8B models, SATS demonstrated improved quality over percentile-based thresholding at matched sparsity levels, with llama 3.1 8B showing an average downstream accuracy increase from 75.44 to 76.2 at 50% sparsity. Token routing further enhanced the quality-throughput trade-off, for instance, boosting Qwen 3 8B's average accuracy from 74.31 to 75.11.

Key takeaway

For Machine Learning Engineers optimizing LLM inference, consider implementing Sensitivity-Aware Thresholding for Sparsity (SATS) to achieve better quality at desired sparsity levels compared to percentile-based methods. You should also explore token routing to dynamically apply sparse computation per token, significantly improving the quality-throughput trade-off. This approach allows for more efficient resource utilization without sacrificing model performance, especially for models like llama 3.1 8B and Qwen 3 8B.

Key insights

Optimizing LLM inference efficiency requires damage-aware activation sparsification and dynamic token-level routing.

Principles

• MLP output distortion guides thresholding.
• Token-specific routing improves trade-offs.
• Local error tracks end-to-end performance.

Method

SATS calibrates layerwise gate thresholds by minimizing local MLP output distortion, then selects a global budget to match target sparsity. Token routing uses a lookup table of token-specific excess damage to dynamically choose sparse or dense paths.

In practice

• Apply SATS for better sparse model quality.
• Use token routing for dynamic inference.
• Combine LoRA with sparse kernels for fine-tuned models.

Topics

• LLM Inference Optimization
• Activation Sparsification
• Sensitivity-Aware Thresholding
• Token Routing
• Gated MLP Blocks
• llama 3.1 8B

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.