Charlie Snell

According to our database, Charlie Snell authored at least 13 papers between 2021 and 2024.

Collaborative distances:

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

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

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 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