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Language agents have demonstrated autonomous decision-making abilities by reasoning with foundation models.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht · 2010
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Language models are few-shot butlers
Vincent Micheli and Francois Fleuret · 2021
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Training language models to follow instructions with human feedback
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Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan · 2022
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Fireact: Toward language agent fine-tuning
Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, and Shunyu Yao · 2023
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi · 2023
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Zhihan Liu, Hao Hu, Shenao Zhang, Hongyi Guo, Shuqi Ke, Boyi Liu, and Zhaoran Wang · 2023
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Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng · 2023
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OpenAI · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao · 2023
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Beyond human data: Scaling self-training for problem-solving with language models
Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, et al · 2023
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Multimodal web navigation with instruction-finetuned foundation models
Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum, Yutaka Matsuo, Aleksandra Faust, Shixiang Shane Gu, and Izzeddin Gur · 2024
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V-star: Training verifiers for self-taught reasoners
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Agentlite: A lightweight library for building and advancing task-oriented llm agent system
Zhiwei Liu, Weiran Yao, Jianguo Zhang, Liangwei Yang, Zuxin Liu, Juntao Tan, Prafulla K Choubey, Tian Lan, Jason Wu, Huan Wang, et al · 2024
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Agentboard: An analytical evaluation board of multi-turn llm agents
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Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
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Voyager: An open-ended embodied agent with large language models
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Agent lumos: Unified and modular training for open-source language agents
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Agenttuning: Enabling generalized agent abilities for llms
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Self-play fine-tuning converts weak language models to strong language models, 2024
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Self-rewarding language models
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Gpt-4v(ision) is a generalist web agent, if grounded
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