Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

1 code implementation • 19 Dec 2023 • Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, Jun Wang

StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.

Language Modelling Large Language Model +2

Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

no code implementations • 15 Nov 2023 • Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou, Jingren Zhou

Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods.

TAG

Qwen Technical Report

2 code implementations • 28 Sep 2023 • Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, Tianhang Zhu

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.

Language Modelling Large Language Model +2

#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models

1 code implementation • 14 Aug 2023 • Keming Lu, Hongyi Yuan, Zheng Yuan, Runji Lin, Junyang Lin, Chuanqi Tan, Chang Zhou, Jingren Zhou

Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data.

Instruction Following TAG

Large Sequence Models for Sequential Decision-Making: A Survey

no code implementations • 24 Jun 2023 • Muning Wen, Runji Lin, Hanjing Wang, Yaodong Yang, Ying Wen, Luo Mai, Jun Wang, Haifeng Zhang, Weinan Zhang

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.

Decision Making

Contextual Transformer for Offline Meta Reinforcement Learning

no code implementations • 15 Nov 2022 • Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, Yaodong Yang

Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.

D4RL Meta Reinforcement Learning +4

Scalable Model-based Policy Optimization for Decentralized Networked Systems

2 code implementations • 13 Jul 2022 • Yali Du, Chengdong Ma, Yuchen Liu, Runji Lin, Hao Dong, Jun Wang, Yaodong Yang

Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.

Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

1 code implementation • 30 May 2022 • Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang

In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.

Decision Making Multi-agent Reinforcement Learning +2

Fully Decentralized Model-based Policy Optimization with Networked Agents

no code implementations • 29 Sep 2021 • Yuchen Liu, Yali Du, Runji Lin, Hangrui Bi, Mingdong Wu, Jun Wang, Hao Dong

Model-based RL is an effective approach for reducing sample complexity.

Model-based Reinforcement Learning Reinforcement Learning (RL)