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Adapting Large Language Models (LLMs) for agent tasks is critical in developing language agents.
Rank analysis of incomplete block designs: I. the method of paired comparisons
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Efficient training of artificial neural networks for autonomous navigation
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Apprenticeship learning via inverse reinforcement learning
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Maximum entropy inverse reinforcement learning
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Alfworld: Aligning text and embodied environments for interactive learning
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Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
Brian D. Ziebart. 2010 · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
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Preference-based reinforcement learning: a formal framework and a policy iteration algorithm
Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng, and Sang-Hyeun Park. 2012 · 2012
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon. 2016 · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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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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A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, and Shixiang Gu. 2020 · 2020
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Error bounds of imitating policies and environments
Tian Xu, Ziniu Li, and Yang Yu. 2020 · 2020
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Offline preference-based apprenticeship learning
Daniel Shin, Daniel S Brown, and Anca D Dragan. 2021 · 2021
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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A generalist agent
Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas. 2022 · 2022
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Scienceworld: Is your agent smarter than a 5th grader?
Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang, Nicholas Roy, and Chuchu Fan. 2024 · 2024
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Mimir: A streamlined platform for personalized agent tuning in domain expertise
Chunyuan Deng, Xiangru Tang, Yilun Zhao, Hanming Wang, Haoran Wang, Wangchunshu Zhou, Arman Cohan, and Mark Gerstein. 2024 · 2024
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Inverse preference learning: Preference-based rl without a reward function
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Cmat: A multi-agent collaboration tuning framework for enhancing small language models
Xuechen Liang, Meiling Tao, Tianyu Shi, and Yiting Xie. 2024 · 2024
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Simpo: Simple preference optimization with a reference-free reward
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Fireact: Toward language agent fine-tuning
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Pangu-agent: A fine-tunable generalist agent with structured reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, et al. 2023 · 2023
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Contrastive prefence learning: Learning from human feedback without rl
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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, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Cognitive architectures for language agents
Theodore R Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Tool learning with large language models: A survey
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Small llms are weak tool learners: A multi-llm agent
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Trial and error: Exploration-based trajectory optimization for llm agents
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Scaling instructable agents across many simulated worlds
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Embodied multi-modal agent trained by an llm from a parallel textworld
Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lusong Li, Li Shen, Xiaodong He, Jing Jiang, and Yuhui Shi. 2024 · 2024
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A survey on the memory mechanism of large language model based agents
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Few-shot preference learning for human-in-the-loop rl
Donald Joseph Hejna III and Dorsa Sadigh. 2023 · 2025
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