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Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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Conditional computation in neural networks for faster models
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Adaptive computation time for recurrent neural networks
Alex Graves · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
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Conditional deep learning for energy-efficient and enhanced pattern recognition
Priyadarshini Panda, Abhronil Sengupta, and Kaushik Roy · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and J. Dean · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Universal transformers
Mostafa Dehghani, Stephan Gouws, O. Vinyals, Jakob Uszkoreit, and Lukasz Kaiser · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Chen Xing, Devansh Arpit, Christos Tsirigotis, and Yoshua Bengio · 2018
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Fast transformer decoding: One write-head is all you need
Noam Shazeer · 2019
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Tied transformers: Neural machine translation with shared encoder and decoder
Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Depth-adaptive transformer
Maha Elbayad, Jiatao Gu, Edouard Grave, and Michael Auli · 2020
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Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
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Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean · 2021
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Lessons on parameter sharing across layers in transformers
Sho Takase and Shun Kiyono · 2021
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Stablemoe: Stable routing strategy for mixture of experts
Damai Dai, Li Dong, Shuming Ma, Bo Zheng, Zhifang Sui, Baobao Chang, and Furu Wei · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
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The lazy neuron phenomenon: On emergence of activation sparsity in transformers
Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J Reddi, Ke Ye, Felix Chern, Felix Yu, Ruiqi Guo, et al · 2022
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Efficiently scaling transformer inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean · 2022
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Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale
Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, and Yuxiong He · 2022
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Confident adaptive language modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, and Donald Metzler · 2022
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 2022
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Orca: A distributed serving system for { \{ Transformer-Based } \} generative models
Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, and Byung-Gon Chun · 2022
Cited alongside, same era.
Mixture-of-experts with expert choice routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Quoc V Le, James Laudon, et al · 2022
Cited alongside, same era.
St-moe: Designing stable and transferable sparse expert models
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, and William Fedus · 2022
Cited alongside, same era.
GQA: training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai · 2023
Cited alongside, same era.
Fast and robust early-exiting framework for autoregressive language models with synchronized parallel decoding
Sangmin Bae, Jongwoo Ko, Hwanjun Song, and Se-Young Yun · 2023
Can looped transformers learn to implement multi-step gradient descent for in-context learning?
Khashayar Gatmiry, Nikunj Saunshi, Sashank J Reddi, Stefanie Jegelka, and Sanjiv Kumar · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Google Gemini Team · 2024
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Training large language models to reason in a continuous latent space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian · 2024
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Kvquant: Towards 10 million context length llm inference with kv cache quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, and Amir Gholami · 2024
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Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2023
Cited alongside, same era.
Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
Cited alongside, same era.
Model tells you what to discard: Adaptive kv cache compression for llms
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao · 2023
Cited alongside, same era.
Do generative large language models need billions of parameters?
Sia Gholami and Marwan Omar · 2023
Cited alongside, same era.
Looped transformers as programmable computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papailiopoulos · 2023
Cited alongside, same era.
Think before you speak: Training language models with pause tokens
Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, and Vaishnavh Nagarajan · 2023
Cited alongside, same era.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Gear: An efficient kv cache compression recipe for near-lossless generative inference of llm
Hao Kang, Qingru Zhang, Souvik Kundu, Geonhwa Jeong, Zaoxing Liu, Tushar Krishna, and Tuo Zhao · 2024
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The llama 3 herd of models
AI @ Meta Llama Team · 2024
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γ \gamma -mod: Exploring mixture-of-depth adaptation for multimodal large language models
Yaxin Luo, Gen Luo, Jiayi Ji, Yiyi Zhou, Xiaoshuai Sun, Zhiqiang Shen, and Rongrong Ji · 2024
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Performance control in early exiting to deploy large models at the same cost of smaller ones
Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, Joao Monteiro, and Valentina Zantedeschi · 2024
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Training of physical neural networks
Ali Momeni, Babak Rahmani, Benjamin Scellier, Logan G. Wright, Peter L. McMahon, Clara C. Wanjura, Yuhang Li, Anas Skalli, Natalia G. Berloff, Tatsuhiro Onodera, Ilker Oguz, Francesco Morichetti, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Cheng Zhang, Danijela Marković, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea J. Liu, Demetri Psaltis, Andrea Alù, and Romain Fleury · 2024
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Dynamic memory compression: Retrofitting llms for accelerated inference
Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, and Edoardo M Ponti · 2024
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Loop neural networks for parameter sharing
Kei-Sing Ng and Qingchen Wang · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf · 2024
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Let’s think dot by dot: Hidden computation in transformer language models
Jacob Pfau, William Merrill, and Samuel R Bowman · 2024
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Mixture-of-depths: Dynamically allocating compute in transformer-based language models
David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap, Peter Conway Humphreys, and Adam Santoro · 2024
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You only cache once: Decoder-decoder architectures for language models
Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, and Furu Wei · 2024
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Auxiliary-loss-free load balancing strategy for mixture-of-experts
Lean Wang, Huazuo Gao, Chenggang Zhao, Xu Sun, and Damai Dai · 2024
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Layer-condensed kv cache for efficient inference of large language models
Haoyi Wu and Kewei Tu · 2024
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Pyramidinfer: Pyramid kv cache compression for high-throughput llm inference
Dongjie Yang, XiaoDong Han, Yan Gao, Yao Hu, Shilin Zhang, and Hai Zhao · 2024
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Quiet-star: Language models can teach themselves to think before speaking
Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah D Goodman · 2024
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{ \{ DistServe } \} : Disaggregating prefill and decoding for goodput-optimized large language model serving
Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, and Hao Zhang · 2024
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Abbie: Autoregressive block-based iterative encoder for efficient sequence modeling
Preslav Aleksandrov, Meghdad Kurmanji, Fernando Garcia Redondo, David O’Shea, William Shen, Alex Iacob, Lorenzo Sani, Xinchi Qiu, Nicola Cancedda, and Nicholas D Lane · 2025
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Inner thinking transformer: Leveraging dynamic depth scaling to foster adaptive internal thinking
Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, and Haifeng Wang · 2025
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S-grpo: Early exit via reinforcement learning in reasoning models
Muzhi Dai, Chenxu Yang, and Qingyi Si · 2025
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Scaling up test-time compute with latent reasoning: A recurrent depth approach
Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein · 2025
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Google Gemini Team · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Flashthink: An early exit method for efficient reasoning
Guochao Jiang, Guofeng Quan, Zepeng Ding, Ziqin Luo, Dixuan Wang, and Zheng Hu · 2025
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Reasoning with latent thoughts: On the power of looped transformers
Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, and Sashank J Reddi · 2025
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Codi: Compressing chain-of-thought into continuous space via self-distillation
Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, and Yulan He · 2025
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Llm pretraining with continuous concepts
Jihoon Tack, Jack Lanchantin, Jane Yu, Andrew Cohen, Ilia Kulikov, Janice Lan, Shibo Hao, Yuandong Tian, Jason Weston, and Xian Li · 2025
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Dynamic early exit in reasoning models
Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Qiaowei Li, Zheng Lin, Li Cao, and Weiping Wang · 2025
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Pretraining language models to ponder in continuous space
Boyi Zeng, Shixiang Song, Siyuan Huang, Yixuan Wang, He Li, Ziwei He, Xinbing Wang, Zhiyu Li, and Zhouhan Lin · 2025
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