Mixture-of-Experts Quantization

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 16

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TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
Oct 6, 2026Xin Wang, Hao Yu, Zhengyang Zhuge +9LLM Quantization Quantization-Aware Training
Alibaba Token Hub, Alibaba Group · Ohio State University
Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing
Sep 16, 2026Eunju Shin, Jongbin RyuMixed-Precision Quantization Mixture-of-Experts Quantization
Ajou University, South Korea
Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Sep 14, 2026Zhenghong Huang, Hongfan Wu, Jiheng ZhangMixture-of-Experts Inference Mixture-of-Experts Quantization
The Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong
Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs
Aug 31, 2026Deokjae Lee, Sihun Chu, Hyun Oh SongMixed-Precision Quantization LLM Quantization
Department of Computer Science and Engineering, Seoul National University · Neural Processing Research Center · 3Neural Processing Research Center +2
Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving
Aug 9, 2026Matteo GrellaTernary Quantization Mixture-of-Experts Inference
Crisis24
RotaryQuant: Fitting 120B MoE Models on Consumer Hardware via Fused Compressed-Space Attention
Aug 8, 2026Anthony. Lui, Mohamed. Elsaied, N. P. SavaniExpert Offloading Mixture-of-Experts Language Models
Cognizant AI & Analytics, London, UB8 3PH, UK · Southern Methodist University (SMU), Engineering Management, Lyle School of Engineering, Dallas, TX, USA · Cognizant AI Lab, Responsible AI Office, San Francisco, USA +1
PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization
Jul 17, 2026Yuchen Yang, Yifan Zhao, Anisha Dasgupta +1LLM Quantization Mixture-of-Experts Language Models
University of Illinois Urbana-Champaign
MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
Jun 15, 2026Yuanteng Chen, Peisong Wang, Zhilei Liu +9Mixed-Precision Quantization LLM Quantization
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · 3Zhongguancun Academy
Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models
Jun 4, 2026Hancheol Park, Geonho Lee, Tairen Piao +1LLM Quantization Mixture-of-Experts Quantization
Nota Inc., South Korea.
AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization
Jun 3, 2026Wanqi Yang, Yuexiao Ma, Alexander Conzelmann +4Mixed-Precision Quantization Mixture-of-Experts Language Models
Max Planck Institute for Intelligent Systems · ELLIS Institute Tübingen · Tübingen AI Center +5
BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization
May 22, 2026Jiayu Zhao, Zihan Teng, Minhao Fan +4Mixed-Precision Quantization LLM Quantization
School of Microelectronics, University of Science and Technology of China · College of Computing and Data Science, Nanyang Technological University · School of Electrical and Electronic Engineering, Nanyang Technological University
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
May 21, 2026Jianing Deng, Song Wang, Dongwei Wang +4Mixed-Precision Quantization LLM Quantization
University of Pittsburgh · University of Central Florida · University of Arizona +1
RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
May 14, 2026Zhengjia Zhong, Shuyan Ke, Zaizhou Lin +3Residual VQ Vector Quantization
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China.
TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
May 10, 2026Hongyaoxing Gu, Xinzhe Chen, Lijuan Hu +1Efficient Inference Mixture-of-Experts Inference
Institute of Software Chinese Academy of Sciences · University of Chinese Academy of Sciences
ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits
Jan 20, 2026Aryan KarmoreTernary Quantization Mixture-of-Experts Language Models
Indian Institute of Information Technology, Nagpur
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
Date pendingKexin Chu, Dawei Xiang, Zixu Shen +3Mixed-Precision Quantization GPU Acceleration
University of Connecticut · University of California, Santa Cruz · Independent Researcher

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

Xin Wang, Hao Yu, Zhengyang Zhuge, Bochao Mao+8

Alibaba Token Hub, Alibaba Group · Ohio State University

LLM Quantization Quantization-Aware Training LLM Inference Acceleration Mixture-of-Experts Quantization RL Post-Training

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Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation, which motivates low-precision rollout for efficient RL training. However, existing FP4 RL methods suffer from a key limitation: they primarily optimize quantization accuracy on the training and rollout paths independently rather than directly reducing the discrepancy between the two quantized execution paths. In this work, we propose TRACE (Train-Rollout Quantization Alignment via Compact GuidancE), an FP4 quantization framework for RL training of Mixture-of-Experts (MoE) language models that addresses the limitation of existing FP4 RL methods. TRACE incorporates rollout-guided quantization-aware training that uses rollout-side quantization outcomes to guide training-side FP4 rounding decisions, directly reducing train-rollout discrepancy. Moreover, TRACE adopts an efficient quantization-information caching scheme that selectively retains mantissa and scale information from deeper layers to reduce the storage and communication overhead introduced by rollout guidance. We evaluate TRACE on four large-scale MoE language models across reasoning, coding, and long-horizon RL tasks. Our results demonstrate that TRACE enables joint FP4 weight/activation and FP4 KV-cache rollout with RL performance comparable to BF16 rollout, while achieving up to 5.4xrollout speedup and strong final FP4 performance compared with post-hoc FP4 quantization of BF16-trained policies.

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