26 citations · 58 across the 13 of their papers we have counts for
13 papers
OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents
Ruixun Liu, Yuxuan Wang, Jiacheng Xie +10
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, i…
Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Jiazhao Zhang, Gengze Zhou, Hale Yin +32
Agentic navigation systems require a base navigation model whose observation strategy can be externally reconfigured at inference time, because instruction following, object search…
Qwen3-VL Technical Report
Shuai Bai, Yuxuan Cai, Ruizhe Chen +61
We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…
Group Sequence Policy Optimization
Chujie Zheng, Shixuan Liu, Mingze Li +9
This paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and performant reinforcement learning algorithm for training large language models. Unlike p…
Generalist Reward Models: Found Inside Large Language Models
Yi-Chen Li, Tian Xu, Yang Yu +6
The alignment of Large Language Models (LLMs) is critically dependent on reward models trained on costly human preference data. While recent work explores bypassing this cost with…
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
Songyi Gao, Zuolin Tu, Rong-Jun Qin +3
Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment. To facilitate offline RL research, we previously introd…
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li +4
Reinforcement learning (RL) trains agents to accomplish complex tasks through environmental interaction data, but its capacity is also limited by the scope of the available data. T…
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
Xiong-Hui Chen, Junyin Ye, Hang Zhao +9
Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many…
Language Model Self-improvement by Reinforcement Learning Contemplation
Jing-Cheng Pang, Pengyuan Wang, Kaiyuan Li +4
Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing (NLP) tasks. However, fine-tuning these models often necessitates subs…
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
Xiong-Hui Chen, Bowei He, Yang Yu +5
Long-term user engagement (LTE) optimization in sequential recommender systems (SRS) is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long-term…
A Survey on Model-based Reinforcement Learning
Fan-Ming Luo, Tian Xu, Hang Lai +3
Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in play…
Offline Reinforcement Learning with Causal Structured World Models
Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian +2
Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environme…
Adversarial Counterfactual Environment Model Learning
Xiong-Hui Chen, Yang Yu, Zheng-Mao Zhu +8
A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, rec…