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Model merging, typically on Instruct and Thinking models, has shown remarkable performance for efficient reasoning.
Weight averaging for neural networks and local resampling schemes
Joachim Utans. 1996 · 1996
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A comprehensive survey on model compression and acceleration
Tejalal Choudhary, Vipul Mishra, Anurag Goswami, and Jagannathan Sarangapani. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, and 1 others. 2021 · 2021
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2022 · 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, and 1 others. 2022 · 2022
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Opencompass: A universal evaluation platform for foundation models
OpenCompass Contributors. 2023 · 2023
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Soft merging of experts with adaptive routing
Mohammed Muqeeth, Haokun Liu, and Colin Raffel. 2023 · 2023
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Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023 · 2023
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Adamerging: Adaptive model merging for multi-task learning
Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao. 2023 · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Yu Bowen, Haiyang Yu, Fei Huang, and Yongbin Li. 2023 · 2023
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Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023 · 2023
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Aime problems and solutions
AIME. 2025 · 2024
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Do not think that much for 2+ 3=? on the overthinking of o1-like llms
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, and 1 others. 2024 · 2024
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Arcee’s mergekit: A toolkit for merging large language models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vladimir Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024 · 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 · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and 1 others. 2024 · 2024
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Sketch-of-thought: Efficient llm reasoning with adaptive cognitive-inspired sketching
Simon A Aytes, Jinheon Baek, and Sung Ju Hwang. 2025 · 2025
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Natasha Butt, Ariel Kwiatkowski, Ismail Labiad, Julia Kempe, and Yann Ollivier. 2025 · 2025
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Merge now, regret later: The hidden cost of model merging is adversarial transferability
Ankit Gangwal and Aaryan Ajay Sharma. 2025 · 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, and 1 others. 2025 · 2025
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Tl; dr: Too long, do re-weighting for effcient llm reasoning compression
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Daniel Marczak, Bartłomiej Twardowski, Tomasz Trzciński, and Sebastian Cygert. 2024 · 2024
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Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. 2024 · 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 · 2024
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Fast best-of-n decoding via speculative rejection
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Towards efficient pareto set approximation via mixture of experts based model fusion
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Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, and Dacheng Tao. 2024 · 2024
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 2024 · 2024
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Unlocking efficient long-to-short llm reasoning with model merging
Han Wu, Yuxuan Yao, Shuqi Liu, Zehua Liu, Xiaojin Fu, Xiongwei Han, Xing Li, Hui-Ling Zhen, Tao Zhong, and Mingxuan Yuan. 2025a
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Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Haizhen Huang, Weiwei Deng, Ying Nian Wu, Yeyun Gong, and 1 others. 2025 · 2025
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Stop overthinking: A survey on efficient reasoning for large language models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Na Zou, and 1 others. 2025 · 2025
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Kimi k1. 5: Scaling reinforcement learning with llms
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Sampling-efficient test-time scaling: Self-estimating the best-of-n sampling in early decoding
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Chain of draft: Thinking faster by writing less
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Z1: Efficient test-time scaling with code
Zhaojian Yu, Yinghao Wu, Yilun Zhao, Arman Cohan, and Xiao-Ping Zhang. 2025 · 2025
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