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 001; Alison Gopnik (CogSci, 2021)

Approximating How Single Head Attention Learns. - Charlie Snell; Ruiqi Zhong; Dan Klein 001; Jacob Steinhardt (CoRR, 2021)

Describing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (ICML, 2022)

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. - Charlie Snell; Sherry Yang 001; Justin Fu; Yi Su; Sergey Levine (NAACL-HLT (Findings), 2022)

Summarizing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (CoRR, 2022)

Active Programming by Example with a Natural Language Prior. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jason Eisner (CoRR, 2022)

Learning by Distilling Context. - Charlie Snell; Dan Klein 001; Ruiqi Zhong (CoRR, 2022)

Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jason Eisner (EMNLP, 2023)

Offline RL for Natural Language Generation with Implicit Language Q Learning. - Charlie Snell; Ilya Kostrikov; Yi Su; Sherry Yang 001; Sergey Levine (ICLR, 2023)

The False Promise of Imitating Proprietary LLMs. - Arnav Gudibande; Eric Wallace; Charlie Snell; Xinyang Geng; Hao Liu 055; Pieter Abbeel; Sergey Levine; Dawn Song (CoRR, 2023)

LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models. - Marwa Abdulhai; Isadora White; Charlie Snell; Charles Sun; Joey Hong; Yuexiang Zhai; Kelvin Xu; Sergey Levine (CoRR, 2023)

The False Promise of Imitating Proprietary Language Models. - Arnav Gudibande; Eric Wallace; Charlie Snell; Xinyang Geng; Hao Liu 055; Pieter Abbeel; Sergey Levine; Dawn Song (ICLR, 2024)

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. - Charlie Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (CoRR, 2024)

Predicting Emergent Capabilities by Finetuning. - Charlie Snell; Eric Wallace; Dan Klein 001; Sergey Levine (CoRR, 2024)

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. - Charlie Victor Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (ICLR, 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; et al. (CoRR, 2025)

Value-Based Deep RL Scales Predictably. - Oleh Rybkin; Michal Nauman; Preston Fu; Charlie Snell; Pieter Abbeel; Sergey Levine; Aviral Kumar (CoRR, 2025)

Reasoning Models Can Be Effective Without Thinking. - Wenjie Ma; Jingxuan He; Charlie Snell; Tyler Griggs; Sewon Min; Matei Zaharia (CoRR, 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, 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, 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, 2025)