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Charlie Snell
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The false promise of imitating proprietary llms
A Gudibande, E Wallace, C Snell, X Geng, H Liu, P Abbeel, S Levine, ...
arXiv preprint arXiv:2305.15717, 2023
1822023
Scaling llm test-time compute optimally can be more effective than scaling model parameters
C Snell, J Lee, K Xu, A Kumar
arXiv preprint arXiv:2408.03314, 2024
160*2024
Offline rl for natural language generation with implicit language q learning
C Snell, I Kostrikov, Y Su, M Yang, S Levine
arXiv preprint arXiv:2206.11871, 2022
882022
Learning by distilling context
C Snell, D Klein, R Zhong
arXiv preprint arXiv:2209.15189, 2022
412022
Describing differences between text distributions with natural language
R Zhong, C Snell, D Klein, J Steinhardt
International Conference on Machine Learning, 27099-27116, 2022
412022
Approximating how single head attention learns
C Snell, R Zhong, D Klein, J Steinhardt
arXiv preprint arXiv:2103.07601, 2021
372021
Context-aware language modeling for goal-oriented dialogue systems
C Snell, M Yang, J Fu, Y Su, S Levine
arXiv preprint arXiv:2204.10198, 2022
262022
Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models
M Abdulhai, I White, C Snell, C Sun, J Hong, Y Zhai, K Xu, S Levine
arXiv preprint arXiv:2311.18232, 2023
202023
Non-programmers can label programs indirectly via active examples: A case study with text-to-SQL
R Zhong, C Snell, D Klein, J Eisner
Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023
9*2023
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
V Xiang, C Snell, K Gandhi, A Albalak, A Singh, C Blagden, D Phung, ...
arXiv preprint arXiv:2501.04682, 2025
22025
Predicting emergent capabilities by finetuning
C Snell, E Wallace, D Klein, S Levine
arXiv preprint arXiv:2411.16035, 2024
12024
The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification?
E Kosoy, M Belyi, CV Snell, B Lake, J Tenenbaum, A Gopnik
Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021
12021
Value-Based Deep RL Scales Predictably
O Rybkin, M Nauman, P Fu, C Snell, P Abbeel, S Levine, A Kumar
arXiv preprint arXiv:2502.04327, 2025
2025
Crop Stage Estimation: A Multi-Satellite Historical Model and a Scalable Neural Network Forecaster
N Padmanabhan, A Mahesh, A Sripathy, A Sujithkumar, A Sun, C Snell, ...
AGU Fall Meeting Abstracts 2020, IN011-08, 2020
2020
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Articles 1–14