From the 1 of 8 linked papers with an AI index.
8 papers
DarwinLM: Evolutionary Structured Pruning of Large Language Models
Shengkun Tang, Oliver Sieberling, Eldar Kurtic +2
The paper introduces DarwinLM, an evolutionary search method for training-aware structured pruning of large language models that integrates lightweight post‑pruning training to fin…
A Survey on Diffusion Language Models
Tianyi Li, Mingda Chen, Bowei Guo +1
Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm. By generating tokens in parallel through…
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
Zongfang Liu, Shengkun Tang, Boyang Sun +2
Sparse Mixture-of-Experts (SMoE) language models achieve strong capability at low per-token compute, yet deployment remains constrained by memory footprint and throughput because t…
BiGain: Unified Token Compression for Joint Generation and Classification
Jiacheng Liu, Shengkun Tang, Jiacheng Cui +2
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity…
Diff-ES: Stage-wise Structural Diffusion Pruning via Evolutionary Search
Zongfang Liu, Shengkun Tang, Zongliang Wu +2
Diffusion models have achieved remarkable success in high-fidelity image generation but remain computationally demanding due to their multi-step denoising process and large model s…
Sink-Aware Pruning for Diffusion Language Models
Aidar Myrzakhan, Tianyi Li, Bowei Guo +2
Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressi…