Fetching the paper…
Reading the bibliography…
Test-Time Scaling (TTS) has proven effective in improving the performance of Large Language Models (LLMs) during inference.
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 · 2021
Earlier work this paper cites.
Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, and 1 others. 2021 · 2021
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, and 29 others. 2023 · 2023
Earlier work this paper cites.
Accelerating large language model decoding with speculative sampling
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. 2023 · 2023
Earlier work this paper cites.
Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Earlier work this paper cites.
Hydra: Sequentially-dependent draft heads for medusa decoding
Zachary Ankner, Rishab Parthasarathy, Aniruddha Nrusimha, Christopher Rinard, Jonathan Ragan-Kelley, and William Brandon. 2024 · 2024
Earlier work this paper cites.
Aime 2024 dataset
AoPS. 2024 · 2024
Earlier work this paper cites.
Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024
Earlier work this paper cites.
Medusa: Simple llm inference acceleration framework with multiple decoding heads
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao. 2024 · 2024
Earlier work this paper cites.
Introducing llama 3.1: Our most capable models to date
Meta. 2024 · 2024
Earlier work this paper cites.
Learning to reason with llms
OpenAI. September 2024 · 2024
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 22 others. 2024 · 2024
Cited alongside, same era.
Generative verifiers: Reward modeling as next-token prediction
Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, and Xinyun Chen. 2025 · 2025
Closest in time.
EAGLE-3: Scaling up inference acceleration of large language models via training-time test
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. 2025 · 2025
Closest in time.
Can 1b llm surpass 405b llm? rethinking compute-optimal test-time scaling
Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, and Bowen Zhou. 2025 · 2025
Closest in time.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025 · 2025
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. 2024 · 2024
Cited alongside, same era.
Aime 2025 dataset
AoPS. 2025 · 2025
Cited alongside, same era.
Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C Park, and Youngjin Kwon. 2025 · 2025
Cited alongside, same era.
rstar-math: Small llms can master math reasoning with self-evolved deep thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang. 2025 · 2025
Cited alongside, same era.
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
Cited alongside, same era.
Moesd: Unveil speculative decoding’s potential for accelerating sparse moe
Zongle Huang, Lei Zhu, Zongyuan Zhan, Ting Hu, Weikai Mao, Xianzhi Yu, Yongpan Liu, and Tianyu Zhang. 2025 · 2025
Cited alongside, same era.
EAGLE-2: Faster inference of language models with dynamic draft trees
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. 2024a
Cited in the paper.
EAGLE: Speculative sampling requires rethinking feature uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. 2024b
Cited in the paper.
Amrith Setlur, Nived Rajaraman, Sergey Levine, and Aviral Kumar. 2025 · 2025
Closest in time.
Heimdall: test-time scaling on the generative verification
Wenlei Shi and Xing Jin. 2025 · 2025
Closest in time.
Nishad Singhi, Hritik Bansal, Arian Hosseini, Aditya Grover, Kai-Wei Chang, Marcus Rohrbach, and Anna Rohrbach. 2025 · 2025
Closest in time.
Qwq-32b: Embracing the power of reinforcement learning
Qwen Team. 2025 · 2025
Closest in time.
Towards thinking-optimal scaling of test-time compute for llm reasoning
Wenkai Yang, Shuming Ma, Yankai Lin, and Furu Wei. 2025 · 2025
Closest in time.
Learning harmonized representations for speculative sampling
Lefan Zhang, Xiaodan Wang, Yanhua Huang, and Ruiwen Xu. 2025 · 2025
Closest in time.