Yueqing Sun

EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds

Runxuan Liu, Jingqi Shi, Baoxin Wang +10

Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation b…

Consistent Plan-Act for Long-Horizon Agentic Tasks

Heng-Zhuang Li, Yi-Kai Zhang, Yu Wang +4

Long-horizon agentic tasks demand strong reasoning and efficient execution across successive interactions with dynamic environments. A common approach decouples high-level planning…

cs.CV2026

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

Tianjie Ju, Zheng Wu, Yueqing Sun +15

Multimodal large language models (MLLMs) can interpret a street view, but reliable urban action depends on whether such local evidence remains useful after the agent starts to move…

MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models

Zhekai Wang, Haoxiang Huang, Xiang Liu +6

Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We i…

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao +10

Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes…

Distill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance

Zhuowen Han, Jinwei Xiao, Zhengxi Lu +9

Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models (LLMs). While Group Relative Policy Optimization (GRPO)…

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13

Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independen…

CAST: Game Solvers as Turn-Level Teachers for LLM Agents

Yu Wang, Yi-Kai Zhang, Wentao Shi +8

Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR)…

Finding the Evidence: Discovering Decision-Supporting Tokens for On-Policy Reasoning Distillation

Jinwei Xiao, Zhuowen Han, Yueqing Sun +6

On-policy distillation transfers reasoning ability through dense token-level supervision, yet the nature of the transferable signal remains unclear. We discover that reasoning chai…

cs.CV2026

Semantic-Enriched Latent Visual Reasoning

Tianrun Xu, Yue Sun, Qixun Wang +8

Multimodal latent-space reasoning aims to replace explicit thinking with images by performing visual reasoning directly in a compact latent space. However, existing approaches larg…

MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning

Yuxin Liu, Ziang Ye, Yueqing Sun +6

Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforeh…

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

Tianjie Ju, Yueqing Sun, Zheng Wu +7

Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic op…

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training

Tianhao Hu, Xiangcheng Liu, Yuchun Miao +16

Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed ge…

$V_{0}$ : A Generalist Value Model for Any Policy at State Zero

Yi-Kai Zhang, Zhiyuan Yao, Hongyan Hao +6

Policy gradient methods rely on a baseline to measure the relative advantage of an action, ensuring the model reinforces behaviors that outperform its current average capability. I…

$V_{0.5}$ : Generalist Value Model as a Prior for Sparse RL Rollouts

Yi-Kai Zhang, Yueqing Sun, Hongyan Hao +4

In Reinforcement Learning with Verifiable Rewards (RLVR), constructing a robust advantage baseline is critical for policy gradients, effectively guiding the policy model to reinfor…

TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training

Jinluan Yang, Yuxin Liu, Zhengyu Chen +7

Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected…

ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training

Dunwei Tu, Hongyan Hao, Hansi Yang +10

Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scar…

CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs

Zhiyuan Yao, Yi-Kai Zhang, Yuxin Chen +7

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning. However, standard frameworks like Group Relative Policy Optimizatio…

P-EAGLE: Parallel-Drafting EAGLE with Scalable Training

Mude Hui, Xin Huang, Jaime Campos Salas +5

Reasoning LLMs produce longer outputs, requiring speculative decoding drafters trained on extended sequences. Parallel drafting - predicting multiple tokens per forward pass - offe…