pruning

论文提出 WISD（Weakening-Induced On-Policy Self-Distillation），通过将大推理模型与量化的弱化版本进行对比生成训练信号，在无需特权数据的情况下实现自蒸馏，在数学与代码推理基准上平均提升 4.0–5.5 个百分点并达到最先进性能。

We introduce TWIG (Tree-structured Weights with Inherited Gates), a representation where every neural network weight is the endpoint of a path of shared discrete decisions, unifying pruning, quantization, low-rank factorization, and weight sharing into a single editable object optimizable via higher-order Boltzmann machine ground-state solvers, achieving 9–15% fewer additions than GPTQ at equal LLM layer error.

该论文系统比较了 2:4 半结构化稀疏化与量化在 LLM 推理中的取舍，发现在仍有更低精度可用时，加入 2:4 稀疏化既更慢又更不准确；只有在达到硬件量化下限（如 FP8/NVFP4）后，2:4 稀疏化才展现出 1.3–1.7× 的线性层加速价值，且需通过重训练恢复质量。

提出了SparseDecoding，一种面向解码阶段的训练无关剪枝框架，通过在自回归生成时收集校准数据对齐Hessian分布，并开发优化的N:M稀疏矩阵向量核，在A100上实现最高1.48倍端到端解码加速。

Nokia Applied Research and Florida State University researchers present capability scaling-down laws for LLM compression (pruning, quantization, distillation), showing that sharing density-response relations halves the measurements needed to fit pruning predictors while matching fully fitted regressions within 0.020 nats per token on Pythia and OLMo-2 models.

Released GSQ-RCO quantized and expert-pruned versions of Qwen3.8-Flash-Next, achieving BF16-level performance at reduced bit-widths and enabling deployment on smaller hardware.

RAZOR is a training-free method for pruning replaceable experts in Mixture-of-Experts LLMs by assessing functional replaceability, achieving superior performance on reasoning tasks compared to existing methods.

ToMoE is a method that converts dense large language models into mixture-of-experts models through dynamic structural pruning, achieving strong performance without weight updates and outperforming state-of-the-art pruning and MoE techniques.

The paper introduces Magnitude Profile (MP) scoring, a calibration-free method for pruning attention heads in transformers, achieving better perplexity on models like OPT-6.7B and RoBERTa-large compared to existing methods, with zero forward passes or calibration data.

The paper introduces Neural Spectral Capacity (NSC), a training-free metric based on the singular-value spectrum to evaluate and optimize neural network architectures, with a dynamic programming method for optimal design under constraints.

tool-prune is a tool that prunes tool schemas before calling local LLM models, reducing prompt tokens by 92% and avoiding hallucinations with no extra LLM round-trips.

The paper introduces a layer-wise curriculum learning method for efficient LLM compression, achieving state-of-the-art performance with significant reductions in GPU memory usage and training time.

This paper proposes FairCompressAgent, an agentic framework that uses a language-model planner to optimize fairness-aware model compression for FPGA deployment, balancing accuracy, fairness, and efficiency.

This paper presents theoretical guarantees for one-shot magnitude pruning and compute-adaptive early exit in neural networks, analyzing compute-accuracy tradeoffs and deriving scaling laws through asymptotic and deep network models.

LILA is a calibration-free structured pruning method for large language models that uses latent spectral geometry to score neuron importance, achieving competitive performance without calibration data.

This paper proposes a damage-aware multi-armed bandit method for structured post-training pruning of vision and language transformers, showing reduced performance degradation compared to baseline approaches in experiments across various models and datasets.

The paper introduces a unified Bayesian variational framework combining spike-and-slab sparsity and Gaussian mixture quantization for high compression rates in large neural networks with minimal accuracy loss.

Debias-SparseGPT is a bias-aware pruning method for large language models that reduces bias amplification from compression techniques like SparseGPT while maintaining model performance and efficiency.

This paper proposes a Generalized Optimization Engine (GOE) to accelerate AI inference on resource-constrained edge devices by integrating various model compression techniques, demonstrating that the choice of compression method affects task accuracy for language models deployed on GPU-less CPUs.

This article examines the Engram component in the Qwen 3.8 Next AI model, detailing it as a 51B-parameter Zipfian cache with highly skewed access patterns, and evaluates optimization through frequency pruning for compression.