Hambro, Eric

LLaMA: Open and Efficient Foundation Language Models

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4,602

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Samvelyan, M., Kirk, R., Kurin, V., Parker-Holder, J., Jiang, M., Hambro, E., Petroni, F., Küttler, H., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 20

Understanding the Effects of RLHF on LLM Generalisation and Diversity

Kirk, R., Mediratta, I., Nalmpantis, C., Luketina, J., Hambro, E., Grefenstette, E., & Raileanu, R. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 17

Teaching Large Language Models to Reason with Reinforcement Learning

Havrilla, A., Du, Y., Raparthy, S. C., Nalmpantis, C., Dwivedi-Yu, J., Zhuravinskyi, M., Hambro, E., Sukhbaatar, S., & Raileanu, R. (2024) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 3

Insights From the NeurIPS 2021 NetHack Challenge

Hambro, E., Mohanty, S., Babaev, D., Byeon, M., Chakraborty, D., Grefenstette, E., Jiang, M., Jo, D., Kanervisto, A., Kim, J., et al. (2022) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1