Charlie Snell

According to our database, Charlie Snell authored at least 22 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Composer 2 Technical Report.

Aaron Chan, Ahmed Shalaby, Alexander Wettig, Aman Sanger, Andrew Zhai, Anurag Ajay, Ashvin Nair, Charlie Snell, Chen Lu, Chen Shen, Emily Jia, Federico Cassano, Hanpeng Liu, Haoyu Chen, Henry Wildermuth, Jacob Jackson, Janet Li, Jediah Katz, Jiajun Yao, Joey Hejna, Josh Warner, Julius Vering, Kevin Frans, Lee Danilek, Less Wright, Lujing Cen, Luke Melas-Kyriazi, Michael Truell, Michiel de Jong, Naman Jain, Nate Schmidt, Nathan Wang, Niklas Muennighoff, Oleg Rybkin, Paul Loh, Phillip Kravtsov, Rishabh Yadav, Sahil Shah, Sam Kottler, Alexander M. Rush, Shengtong Zhang, Shomil Jain, Sriram Sankar, Stefan Heule, Stuart H. Sul, Sualeh Asif, Victor Rong, Wanqi Zhu, William Lin, Yuchen Wu, Yuri Volkov, Yury Zemlyanskiy, Zack Holbrook, Zhiyuan Zhang

CoRR, March, 2026

2025

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs.

Amrith Setlur, Matthew Y. R. Yang, Charlie Snell, Jeremy Greer, Ian Wu, Virginia Smith, Max Simchowitz, Aviral Kumar

CoRR, June, 2025

Learning Adaptive Parallel Reasoning with Language Models.

Jiayi Pan, Xiuyu Li, Long Lian, Charlie Snell, Yifei Zhou, Adam Yala, Trevor Darrell, Kurt Keutzer, Alane Suhr

CoRR, April, 2025

Sleep-time Compute: Beyond Inference Scaling at Test-time.

Kevin Lin, Charlie Snell, Yu Wang, Charles Packer, Sarah Wooders, Ion Stoica, Joseph E. Gonzalez

CoRR, April, 2025

Reasoning Models Can Be Effective Without Thinking.

Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, Matei Zaharia

CoRR, April, 2025

Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought.

Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Fränken, Nick Haber, Chelsea Finn

CoRR, January, 2025

Value-Based Deep RL Scales Predictably.

Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Victor Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar

Proceedings of the Forty-second International Conference on Machine Learning, 2025

LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models.

Marwa Abdulhai, Isadora White, Charlie Victor Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning.

Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Predicting Emergent Capabilities by Finetuning.

Charlie Snell, Eric Wallace, Dan Klein, Sergey Levine

CoRR, 2024

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar

CoRR, 2024

The False Promise of Imitating Proprietary Language Models.

Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

The False Promise of Imitating Proprietary LLMs.

Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song

CoRR, 2023

Offline RL for Natural Language Generation with Implicit Language Q Learning.

Charlie Snell, Ilya Kostrikov, Yi Su, Sherry Yang, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL.

Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Learning by Distilling Context.

Charlie Snell, Dan Klein, Ruiqi Zhong

CoRR, 2022

Active Programming by Example with a Natural Language Prior.

Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner

CoRR, 2022

Summarizing Differences between Text Distributions with Natural Language.

Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt

CoRR, 2022

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems.

Charlie Snell, Sherry Yang, Justin Fu, Yi Su, Sergey Levine

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

Describing Differences between Text Distributions with Natural Language.

Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt

Proceedings of the International Conference on Machine Learning, 2022

2021

Approximating How Single Head Attention Learns.

Charlie Snell, Ruiqi Zhong, Dan Klein, Jacob Steinhardt

CoRR, 2021

The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification?

Eliza Kosoy, Masha Belyi, Charlie Snell, Brenden M. Lake, Josh Tenenbaum, Alison Gopnik

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021