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Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks.
Observational overfitting in reinforcement learning
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, and Behnam Neyshabur. 2019 · 1912
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018 · 2018
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
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A systematic investigation of commonsense knowledge in large language models
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, and Aida Nematzadeh. 2021 · 2021
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Post-hoc interpretability for neural nlp: A survey
Andreas Madsen, Siva Reddy, and A. P. Sarath Chandar. 2021 · 2021
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Text-based rl agents with commonsense knowledge: New challenges, environments and baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, and Murray Campbell. 2021 · 2021
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Liu Pengfei, Yuan Weizhe, Fu Jinlan, Jiang Zhengbao, Hayashi Hiroaki, and Neubig Graham. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al. 2022 · 2022
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Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin G. Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Martin Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Benqi Wang, and Samuel Weinbach. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022
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Vima: General robot manipulation with multimodal prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan. 2022 · 2022
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
Cited alongside, same era.
LMRL gym: Benchmarks for multi-turn reinforcement learning with language models
Marwa Abdulhai, Isadora White, Charlie Victor Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, and Sergey Levine. 2023 · 2023
Cited alongside, same era.
Grounding large language models in interactive environments with online reinforcement learning
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Language models meet world models: Embodied experiences enhance language models
Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu, Zirui Wang, Zichao Yang, and Zhiting Hu. 2023 · 2023
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Xue Yan, Yan Song, Xinyu Cui, Filippos Christianos, Haifeng Zhang, David Henry Mguni, and Jun Wang. 2023 · 2023
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Llm-grounder: Open-vocabulary 3d visual grounding with large language model as an agent
Jianing Yang, Xuweiyi Chen, Shengyi Qian, Nikhil Madaan, Madhavan Iyengar, David F Fouhey, and Joyce Chai. 2023 · 2023
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Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. 2023 · 2023
Cited alongside, same era.
PALM-E: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. 2023 · 2023
Cited alongside, same era.
Grounded decoding: Guiding text generation with grounded models for robot control
Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, et al. 2023 · 2023
Cited alongside, same era.
Mastering robot manipulation with multimodal prompts through pretraining and multi-task fine-tuning
Jiachen Li, Qiaozi Gao, Michael Johnston, Xiaofeng Gao, Xuehai He, Suhaila Shakiah, Hangjie Shi, Reza Ghanadan, and William Yang Wang. 2023 · 2023
Cited alongside, same era.
Exploring the sensitivity of llms’ decision-making capabilities: Insights from prompt variations and hyperparameters
Manikanta Loya, Divya Sinha, and Richard Futrell. 2023 · 2023
Cited alongside, same era.
Inseq: An interpretability toolkit for sequence generation models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal, Malvina Nissim, and Arianna Bisazza. 2023 · 2023
Cited alongside, same era.
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2023 · 2023
Cited alongside, same era.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Retroformer: Retrospective large language agents with policy gradient optimization
Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, et al. 2023 · 2023
Later among the works it cites.
Large language models for robotics: A survey
Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, and Philip S Yu. 2023 · 2023
Later among the works it cites.
Abel Salinas and Fred Morstatter. 2024 · 2024
Closest in time.
Large language models as generalizable policies for embodied tasks
Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Walter Talbott, Katherine Metcalf, Natalie Mackraz, Devon Hjelm, and Alexander Toshev. 2024 · 2024
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Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, and Bo An. 2024 · 2024
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Entropy-regularized token-level policy optimization for large language models
Muning Wen, Cheng Deng, Jun Wang, Weinan Zhang, and Ying Wen. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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Large language models as commonsense knowledge for large-scale task planning
Zirui Zhao, Wee Sun Lee, and David Hsu. 2024 · 2024
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ArCHer: Training language model agents via hierarchical multi-turn RL
Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar. 2024 · 2024
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