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2020 – today
- 2025
[c7]Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar:
Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. ICLR 2025- [i15]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:
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought. CoRR abs/2501.04682 (2025) - [i14]Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar:
Value-Based Deep RL Scales Predictably. CoRR abs/2502.04327 (2025) - [i13]Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, Matei Zaharia:
Reasoning Models Can Be Effective Without Thinking. CoRR abs/2504.09858 (2025) - [i12]Kevin Lin, Charlie Snell, Yu Wang, Charles Packer, Sarah Wooders, Ion Stoica, Joseph E. Gonzalez:
Sleep-time Compute: Beyond Inference Scaling at Test-time. CoRR abs/2504.13171 (2025) - [i11]Jiayi Pan, Xiuyu Li, Long Lian, Charlie Snell, Yifei Zhou, Adam Yala, Trevor Darrell, Kurt Keutzer, Alane Suhr:
Learning Adaptive Parallel Reasoning with Language Models. CoRR abs/2504.15466 (2025) - 2024
- [c6]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary Language Models. ICLR 2024 - [i10]Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar:
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. CoRR abs/2408.03314 (2024) - [i9]Charlie Snell, Eric Wallace, Dan Klein, Sergey Levine:
Predicting Emergent Capabilities by Finetuning. CoRR abs/2411.16035 (2024) - 2023
- [c5]Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner:
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL. EMNLP 2023: 5126-5152 - [c4]Charlie Snell, Ilya Kostrikov, Yi Su, Sherry Yang, Sergey Levine:
Offline RL for Natural Language Generation with Implicit Language Q Learning. ICLR 2023 - [i8]Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song:
The False Promise of Imitating Proprietary LLMs. CoRR abs/2305.15717 (2023) - [i7]Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine:
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models. CoRR abs/2311.18232 (2023) - 2022
- [c3]Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt:
Describing Differences between Text Distributions with Natural Language. ICML 2022: 27099-27116 - [c2]Charlie Snell, Sherry Yang, Justin Fu, Yi Su, Sergey Levine:
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. NAACL-HLT (Findings) 2022: 2351-2366 - [i6]Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt:
Summarizing Differences between Text Distributions with Natural Language. CoRR abs/2201.12323 (2022) - [i5]Charlie Snell, Mengjiao Yang, Justin Fu, Yi Su, Sergey Levine:
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. CoRR abs/2204.10198 (2022) - [i4]Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner:
Active Programming by Example with a Natural Language Prior. CoRR abs/2205.12422 (2022) - [i3]Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine:
Offline RL for Natural Language Generation with Implicit Language Q Learning. CoRR abs/2206.11871 (2022) - [i2]Charlie Snell, Dan Klein, Ruiqi Zhong:
Learning by Distilling Context. CoRR abs/2209.15189 (2022) - 2021
- [c1]Eliza Kosoy, Masha Belyi, Charlie Snell, Brenden M. Lake, Josh Tenenbaum, Alison Gopnik:
The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification? CogSci 2021 - [i1]Charlie Snell, Ruiqi Zhong, Dan Klein, Jacob Steinhardt:
Approximating How Single Head Attention Learns. CoRR abs/2103.07601 (2021)
Coauthor Index
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