ATLaS: Agent Tuning via Learning Critical Steps

Language models are few-shot learners

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Agents that reduce work and information overload

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Intelligent agents: Theory and practice

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Alfworld: Aligning text and embodied environments for interactive learning

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Adam: A method for stochastic optimization

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Mastering the game of go without human knowledge

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Babyai: A platform to study the sample efficiency of grounded language learning

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Reinforcement learning: An introduction

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Pytorch: An imperative style, high-performance deep learning library

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Grandmaster level in starcraft ii using multi-agent reinforcement learning

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A survey of large language models

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Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2020

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Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 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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Scienceworld: Is your agent smarter than a 5th grader?

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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

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Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models

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Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, and Sergey Levine. 2023 · 2023

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Rest meets react: Self-improvement for multi-step reasoning llm agent

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Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, Manzil Zaheer, Felix Yu, and Sanjiv Kumar. 2023 · 2023

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Fireact: Toward language agent fine-tuning

Original

Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023

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Mistral 7b

Original

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Cited alongside, same era.

Selective reflection-tuning: Student-selected data recycling for LLM instruction-tuning

Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, and Tianyi Zhou. 2024a · 2024

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From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning

Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, and Jing Xiao. 2024d · 2024

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Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model

Original

Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, et al. 2024 · 2024

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Agentboard: An analytical evaluation board of multi-turn llm agents

Original

Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He. 2024 · 2024

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Autoact: Automatic agent learning from scratch for qa via self-planning