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 | 182 | 2023 |
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 | 88 | 2022 |
Learning by distilling context C Snell, D Klein, R Zhong arXiv preprint arXiv:2209.15189, 2022 | 41 | 2022 |
Describing differences between text distributions with natural language R Zhong, C Snell, D Klein, J Steinhardt International Conference on Machine Learning, 27099-27116, 2022 | 41 | 2022 |
Approximating how single head attention learns C Snell, R Zhong, D Klein, J Steinhardt arXiv preprint arXiv:2103.07601, 2021 | 37 | 2021 |
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 | 26 | 2022 |
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 | 20 | 2023 |
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 | 2 | 2025 |
Predicting emergent capabilities by finetuning C Snell, E Wallace, D Klein, S Levine arXiv preprint arXiv:2411.16035, 2024 | 1 | 2024 |
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 | 1 | 2021 |
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 |