StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901

Earlier work this paper cites.

Simple statistical gradient-following algorithms for connectionist reinforcement learning

Ronald J Williams. 1992 · 1992

Earlier work this paper cites.

Policy gradient methods for reinforcement learning with function approximation

Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999 · 1999

Earlier work this paper cites.

Deep reinforcement learning from human preferences

Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017

Earlier work this paper cites.

Proximal policy optimization algorithms

Original

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017

Earlier work this paper cites.

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. 2021 · 2021

Earlier work this paper cites.

Learning math reasoning from self-sampled correct and partially-correct solutions

Original

Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov, Christopher Meek, Dragomir Radev, and Jianfeng Gao. 2022 · 2022

Earlier work this paper cites.

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

Earlier work this paper cites.

Solving math word problems with process-and outcome-based feedback

Original

Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022

Earlier work this paper cites.

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

Earlier work this paper cites.

Glm-130b: An open bilingual pre-trained model

Original

Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022 · 2022

Earlier work this paper cites.

Grounding large language models in interactive environments with online reinforcement learning

Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. 2023 · 2023

Earlier work this paper cites.

Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks

Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023 · 2023

Earlier work this paper cites.

Let’s verify step by step

Original

Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023

Cited alongside, same era.

Let’s reward step by step: Step-level reward model as the navigators for reasoning

Original

Qianli Ma, Haotian Zhou, Tingkai Liu, Jianbo Yuan, Pengfei Liu, Yang You, and Hongxia Yang. 2023 · 2023

Cited alongside, same era.

Gpt-4 technical report

Original

OpenAI. 2023 · 2023

Cited alongside, same era.

Gorilla: Large language model connected with massive apis

Original

Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023 · 2023

Cited alongside, same era.

Toolllm: Facilitating large language models to master 16000+ real-world apis

Teaching large language models to reason with reinforcement learning

Original

Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Sainbayar Sukhbaatar, and Roberta Raileanu. 2024 · 2024

Closest in time.

Step-dpo: Step-wise preference optimization for long-chain reasoning of llms

Original

Xin Lai, Zhuotao Tian, Yukang Chen, Senqiao Yang, Xiangru Peng, and Jiaya Jia. 2024 · 2024

Closest in time.

Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets

Original

Zuxin Liu, Thai Hoang, Jianguo Zhang, Ming Zhu, Tian Lan, Shirley Kokane, Juntao Tan, Weiran Yao, Zhiwei Liu, Yihao Feng, et al. 2024 · 2024

Closest in time.

Tool learning with foundation models

Original

Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2024 · 2024