12 papers
TreeCUA: Efficiently Scaling GUI Automation with Tree-Structured Verifiable Evolution
Deyang Jiang, Jing Huang, Xuanle Zhao +6
Effectively scaling GUI automation is essential for computer-use agents (CUAs); however, existing work primarily focuses on scaling GUI grounding rather than the more crucial GUI p…
Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR
Fanfan Liu, Youyang Yin, Peng Shi +3
Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success…
Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective
Kun Chen, Peng Shi, Fanfan Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous…
Learning When to Look: A Disentangled Curriculum for Strategic Perception in Multimodal Reasoning
Siqi Yang, Zilve Gao, Haibo Qiu +5
Multimodal Large Language Models (MLLMs) demonstrate significant potential but remain brittle in complex, long-chain visual reasoning tasks. A critical failure mode is "visual forg…
Context Cascade Compression: Exploring the Upper Limits of Text Compression
Fanfan Liu, Haibo Qiu
Million-level token inputs in long-context tasks pose significant computational and memory challenges for Large Language Models (LLMs). Recently, DeepSeek-OCR conducted research in…
Metis-HOME: Hybrid Optimized Mixture-of-Experts for Multimodal Reasoning
Xiaohan Lan, Fanfan Liu, Haibo Qiu +4
Inspired by recent advancements in LLM reasoning, the field of multimodal reasoning has seen remarkable progress, achieving significant performance gains on intricate tasks such as…
Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model Learning
Haibo Qiu, Xiaohan Lan, Fanfan Liu +4
Recent advancements in large language models (LLMs) have witnessed a surge in the development of advanced reasoning paradigms, which are now being integrated into multimodal large…
Boosting Robotic Manipulation Generalization with Minimal Costly Data
Liming Zheng, Feng Yan, Fanfan Liu +3
The growing adoption of Vision-Language-Action (VLA) models in embodied AI intensifies the demand for diverse manipulation demonstrations. However, high costs associated with data…
RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
Yufeng Zhong, Chengjian Feng, Feng Yan +3
In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents…
RoboTron-Mani: All-in-One Multimodal Large Model for Robotic Manipulation
Feng Yan, Fanfan Liu, Liming Zheng +5
Recently, robotics has advanced significantly through the integration of larger models and large-scale datasets. However, challenges remain in applying these models to 3D spatial i…
RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios
Liming Zheng, Feng Yan, Fanfan Liu +3
Foundation models hold significant potential for enabling robots to perform long-horizon general manipulation tasks. However, the simplicity of tasks and the uniformity of environm…
RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation
Fanfan Liu, Feng Yan, Liming Zheng +3
Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel paradigm, aiming to enhance the model's ability to generalize to new objects and instructions. H…