24 papers
DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models
Zhenglin Wan, Jiayu Chen, Zhengfeiyang Zhang +6
Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assign…
Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions
Chubin Zhang, Zhenglin Wan, Xingrui Yu +4
An agent whose tool keeps returning nothing useful should stop relying on it. In a retrieval environment with controlled source failures, we separate how agents judge results from…
CUA-Sandbox: Efficient Environments for Computer-Use Agent Reinforcement Learning
Xin Yan, Zhengbo Jiao, Jiaqi Liu +11
Reinforcement learning enables computer-use agents to improve through interaction with real software environments, including websites and desktop applications. However, conventiona…
SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models
Enqiao Lu, Xingrui Yu, Yiwei Fu +7
Spiking neural networks (SNNs) offer a path to energy-efficient language modeling through sparse encoding and event-driven computation, but training capable spiking language models…
Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models
Pengfei Zhou, Hexin Wang, Zhengfeiyang Zhang +5
A common strategy for scaling world models is to train on more crawled video with more compute. We argue that this strategy is inefficient: scaling world models also requires a rec…
SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks
Tao Yu, Yifei Qu, Zhiqing Cui +14
Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA e…
Improving Generalization Robustness of Multimodal RLVR
Pengfei Zhou, Zhiwei Tang, Xiaopeng Peng +11
Reinforcement Learning with Verifiable Rewards (RLVR) makes Multimodal Large Language Models more accurate, but the gains are brittle: simply paraphrasing a question or changing th…
SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale
Tong Bai, Zhenglin Wan, Pengfei Zhou +3
Large skill libraries are now standard equipment for LLM agents, and which subset reaches the prompt decides what the agent can do. Enumeration and top- embedding retrieval both…
Agent-as-a-Router: Agentic Model Routing for Coding Tasks
Pengfei Zhou, Zhiwei Tang, Yixing Ma +8
Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all. Con…
Don't Blindly Trust It: How Unreliable Feedback Breaks Tool-Using LLM Agents
Chubin Zhang, Zhenglin Wan, Xingrui Yu +5
Tool-augmented agents are typically evaluated by their gains under reliable external feedback. Yet these gains leave open a key counterfactual: when feedback is unreliable, would t…
Calibration Is Not Control: Intervention Value for LLM-Agent Oversight
Chubin Zhang, Zhenglin Wan, Xingrui Yu +5
Runtime oversight often intervenes when an LLM agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can differ in whether intervention helps.…
DeSRPA: Decoupled Speech Role-Playing Agent via Inference-Time Intervention
Wenqiu Tang, Zhen Wan, Takahiro Komamizu +1
While Large Language Models (LLMs) have revolutionized text-based role-playing, creating immersive Speech Role-Playing Agents (SRPAs) requires a seamless bridge between cognitive r…
Adversarial Dual On-Policy Distillation from Expressive Teacher
Zhenglin Wan, Jingxuan Wu, Xingrui Yu +5
Learning from demonstrations in embodied control is often cast as behavioral cloning, and recent diffusion or flow-matching policies improve this paradigm by modeling multi-modal e…
Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models
Ming Sun, Rui Wang, Xingrui Yu +5
Vision-Language-Action models (VLAs) support generalist robotic control by enabling end-to-end decision policies directly from multi-modal inputs. As trained VLAs are increasingly…
ANO: Robust Policy Optimization via Bounded, Redescending Gain Fields
Yiheng Zhang, Yiming Wang, Kaiyan Zhao +3
Proximal Policy Optimization (PPO) dominates reinforcement learning and LLM alignment, yet its hard-clipping mechanism and unconstrained alternatives (e.g., SPO) sit at two extreme…
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
Xin Yan, Zhenglin Wan, Feiyang Ye +4
Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots an…
SA-VLA: Spatially-Aware Flow-Matching for Vision-Language-Action Reinforcement Learning
Xu Pan, Zhenglin Wan, Xingrui Yu +6
Vision-Language-Action (VLA) models exhibit strong generalization in robotic manipulation, yet reinforcement learning (RL) fine-tuning often degrades robustness under spatial distr…
Denoising Time Matters:Diverse Generation in Diffusion Language Models
Jingxuan Wu, Zhenglin Wan, Xingrui Yu +5
Diffusion language models (Diffusion-LMs) generate text through iterative denoising, exposing a temporal structure that is largely absent from autoregressive decoding. In this pape…
Training Diffusion Policies via Prior-Mapping Co-Evolution
Chubin Zhang, Zhenglin Wan, Feng Chen +7
Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize (e.g., Gaussians) are often too simple to represent the multimodal action distributions…
FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learning
Zhenglin Wan, Jingxuan Wu, Xingrui Yu +4
Flow Matching (FM) has shown remarkable ability in modeling complex distributions and achieves strong performance in offline imitation learning for cloning expert behaviors. Howeve…
Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
Jingxuan Wu, Zhenglin Wan, Xingrui Yu +4
Flow-based text-to-image models follow deterministic trajectories, making it costly to explore diverse modes under limited sampling budgets. Existing approaches to improving divers…
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
Mingcong Lei, Honghao Cai, Yuyuan Yang +16
Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with parti…
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration
Xingrui Yu, Zhenglin Wan, David Mark Bossens +3
Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because…
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
Zhenglin Wan, Xingrui Yu, David Mark Bossens +5
Imitation learning (IL) has shown promise in various applications (e.g. robot locomotion) but is often limited to learning a single expert policy, constraining behavior diversity a…