3 papers
cs.CV2026
OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
Yiduo Jia, Muzhi Zhu, Hao Zhong +7
To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative reasoning, we propose OmniJ…
cs.CV2025
BTL-UI: Blink-Think-Link Reasoning Model for GUI Agent
Shaojie Zhang, Ruoceng Zhang, Pei Fu +8
In the field of AI-driven human-GUI interaction automation, while rapid advances in multimodal large language models and reinforcement fine-tuning techniques have yielded remarkabl…
cs.LG2025
Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle
Linghao Zhu, Yiran Guan, Dingkang Liang +6
Reinforcement learning (RL) has emerged as an effective post-training paradigm for enhancing the reasoning capabilities of multimodal large language model (MLLM). However, current…