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Christopher Hesse
Christopher Hesse
Member of Technical Staff, OpenAI
Verified email at openai.com
Title
Cited by
Cited by
Year
Language models are few-shot learners
T Brown, B Mann, N Ryder, M Subbiah, JD Kaplan, P Dhariwal, ...
Advances in neural information processing systems 33, 1877-1901, 2020
253612020
Evaluating large language models trained on code
M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ...
arXiv preprint arXiv:2107.03374, 2021
20582021
Dota 2 with large scale deep reinforcement learning
C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 2019
17062019
Training verifiers to solve math word problems
K Cobbe, V Kosaraju, M Bavarian, M Chen, H Jun, L Kaiser, M Plappert, ...
arXiv preprint arXiv:2110.14168, 2021
11142021
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
10442023
Openai baselines
P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ...
10012017
Stable baselines
A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ...
8702018
Webgpt: Browser-assisted question-answering with human feedback
R Nakano, J Hilton, S Balaji, J Wu, L Ouyang, C Kim, C Hesse, S Jain, ...
arXiv preprint arXiv:2112.09332, 2021
6992021
Quantifying generalization in reinforcement learning
K Cobbe, O Klimov, C Hesse, T Kim, J Schulman
International conference on machine learning, 1282-1289, 2019
6652019
Leveraging procedural generation to benchmark reinforcement learning
K Cobbe, C Hesse, J Hilton, J Schulman
International conference on machine learning, 2048-2056, 2020
5082020
Scaling laws for autoregressive generative modeling
T Henighan, J Kaplan, M Katz, M Chen, C Hesse, J Jackson, H Jun, ...
arXiv preprint arXiv:2010.14701, 2020
2402020
Gotta learn fast: A new benchmark for generalization in rl
A Nichol, V Pfau, C Hesse, O Klimov, J Schulman
arXiv preprint arXiv:1804.03720, 2018
2032018
Language Models are Few-Shot Learners. 2020. doi: 10.48550
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
arxiv, 5-7, 2005
1532005
Language models are few-shot learners. arXiv
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
Computer Science, Computation and Language, 2005
1522005
Dota 2 with large scale deep reinforcement learning
CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680 2, 2019
1052019
Language models are few-shot learners. CoRR abs/2005.14165 (2020)
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
URL: https://arxiv. org/abs/2005.14165, 2005
812005
Openai baselines (2017)
P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ...
URL https://github. com/openai/baselines, 2016
592016
Language models are few-shot learners
B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ...
arXiv preprint arXiv:2005.14165, 2020
542020
Stable baselines. 2018
A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ...
Publication Title: GitHub repository 21, 2019
422019
Dota 2 with large scale deep reinforcement learning. arXiv 2019
C Berner, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 0
42
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