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Recent advances in depth-recurrent language models show that recurrence can decouple train-time compute and parameter count from test-time compute.
Learning patterns and pattern sequences by self-organizing nets of threshold elements
S-I Amari · 1972
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Neural networks and physical systems with emergent collective computational abilities
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An efficient gradient-based algorithm for on-line training of recurrent network trajectories
Ronald J Williams and Jing Peng · 1990
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Recurrent nets that time and count
Felix A Gers and Jürgen Schmidhuber · 2000
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Loss functions for discriminative training of energy-based models
Yann LeCun and Fu Jie Huang · 2005
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The recurrent temporal restricted boltzmann machine
Ilya Sutskever, Geoffrey E Hinton, and Graham W Taylor · 2008
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Extensions of recurrent neural network language model
Tomáš Mikolov, Stefan Kombrink, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2011
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Net2net: Accelerating learning via knowledge transfer
Tianqi Chen, Ian J. Goodfellow, and Jonathon Shlens · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adaptive computation time for recurrent neural networks
Alex Graves · 2016
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Network morphism
Tao Wei, Changhu Wang, Yong Rui, and Chang Wen Chen · 2016
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Maha Elbayad, Jiatao Gu, Edouard Grave, and Michael Auli · 2019
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Efficient training of bert by progressively stacking
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, and Tieyan Liu · 2019
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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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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 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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The depth-to-width interplay in self-attention
Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, and Amnon Shashua · 2020
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Pedram Zamirai, Jian Zhang, Christopher R Aberger, and Christopher De Sa · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Finetuning pretrained transformers into rnns
Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, and Noah A. Smith · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
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Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, and Tom Goldstein · 2021
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Lessons on parameter sharing across layers in transformers
Sho Takase and Shun Kiyono · 2021
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Path independent equilibrium models can better exploit test-time computation
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J Zico Kolter, and Roger B Grosse · 2022
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End-to-end algorithm synthesis with recurrent networks: Extrapolation without overthinking
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, and Tom Goldstein · 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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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al · 2022
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Saturated transformers are constant-depth threshold circuits
William Merrill, Ashish Sabharwal, and Noah A Smith · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Llama 3.2: Revolutionizing edge ai and vision with open, customizable models, September 2024
Meta · 2024
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Loop neural networks for parameter sharing
Kei-Sing Ng and Qingchen Wang · 2024
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Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, et al · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Anton Lozhkov, Margaret Mitchell, Colin A Raffel, Leandro Von Werra, Thomas Wolf, et al · 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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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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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
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AMD Instinct™ MI300A Accelerators, December 2023
AMD · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
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Cramming: Training a language model on a single gpu in one day
Jonas Geiping and Tom Goldstein · 2023
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Looped transformers as programmable computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papailiopoulos · 2023
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On the inductive bias of stacking towards improving reasoning
Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank Jakkam Reddi, and Sanjiv Kumar · 2024
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How to jointly tune learning rate and weight decay for AdamW
Fabian Schaipp · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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The mamba in the llama: Distilling and accelerating hybrid models
Junxiong Wang, Daniele Paliotta, Avner May, Alexander Rush, and Tri Dao · 2024
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Laco: Large language model pruning via layer collapse
Yifei Yang, Zouying Cao, and Hai Zhao · 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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The hidden drivers of hrm’s performance on arc-agi, August 2025
ARC Prize Team · 2025
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Mixture-of-recursions: Learning dynamic recursive depths for adaptive token-level computation
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Inner thinking transformer: Leveraging dynamic depth scaling to foster adaptive internal thinking
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Do language models use their depth efficiently?
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Math-Verify: Math Verification Library, 2025
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