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Yu Gao, Lixue Gong, Qiushan Guo +28 authors
Seedream 3.0 improves Chinese-English bilingual image generation by enhancing data training, pre-training techniques, and post-training aesthetics, resulting in higher visual quality and faster image generation.
Shengqiong Wu, Weicai Ye, Jiahao Wang +8 authors
Any2Caption framework improves video generation quality and controllability by decoupling condition interpretation and leveraging multimodal large language models.
Guo Chen, Zhiqi Li, Shihao Wang +18 authors
Eagle 2.5 improves long-context multimodal learning through Automatic Degrade Sampling and Image Area Preservation techniques, enhancing VLMs for video and image understanding, and matches state-of-the-art performance on benchmarks.
Xiangyu Zhao, Peiyuan Zhang, Kexian Tang +7 authors
RISEBench evaluates Reasoning-Informed Visual Editing by assessing temporal, causal, spatial, and logical reasoning in multimodal models like GPT-4o-Native.
Yuxuan Luo, Zhengkun Rong, Lizhen Wang +3 authors
A diffusion transformer framework, DreamActor-M1, enhances image-based human animation through hybrid guidance, achieving robust control, multi-scale adaptability, and long-term temporal coherence.
Zhenyi Liao, Qingsong Xie, Yanhao Zhang +4 authors
The study enhances visual-spatial reasoning in multi-modal large language models through GRPO training using the VSI-100k dataset, demonstrating significant performance improvements over base models.
Long Lian, Yifan Ding, Yunhao Ge +8 authors
The Describe Anything Model (DAM) leverages a focal prompt and localized vision backbone to achieve detailed localized captioning, outperforming existing models on various benchmarks through a semi-supervised data pipeline.
Junhao Cheng, Yuying Ge, Yixiao Ge +2 authors
AnimeGamer uses Multimodal Large Language Models (MLLMs) and a video diffusion model to generate contextually consistent, dynamic, and high-quality video clips for an infinite game simulation.
Minghao Wu, Weixuan Wang, Sinuo Liu +7 authors
Research reveals significant disparities in multilingual benchmark evaluations, emphasizing the need for culturally and linguistically tailored benchmarks over translations to achieve equitable technological progress.
Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16 authors
An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
Jiazhan Feng, Shijue Huang, Xingwei Qu +6 authors
ReTool, a tool-integrated learning framework, enhances reasoning models with real-time code execution and reinforcement learning, significantly improving performance in structured problem-solving tasks like mathematical reasoning.
Woongyeong Yeo, Kangsan Kim, Soyeong Jeong +2 authors
UniversalRAG enhances Retrieval-Augmented Generation by dynamically selecting and retrieving knowledge from various heterogeneous and modality-specific sources to improve factual accuracy across different types of queries.
Zhongyang Li, Ziyue Li, Tianyi Zhou
A novel test-time optimization method, C3PO, improves the accuracy and efficiency of Mixture-of-Experts Large Language Models by re-weighting experts based on surrogate objectives from neighboring samples.
Jingcheng Hu, Yinmin Zhang, Qi Han +3 authors
Open-Reasoner-Zero achieves superior performance on reasoning benchmarks using a minimalist PPO approach, requiring fewer training steps than DeepSeek-R1-Zero.
Yash Savani, Asher Trockman, Zhili Feng +4 authors
Antidistillation sampling modifies a model's next-token probability distribution to disrupt the generation of reasoning traces for distillation without affecting model performance.
Ding Chen, Qingchen Yu, Pengyuan Wang +6 authors
xVerify, an efficient answer verifier, demonstrates strong equivalence judgment capabilities in evaluating reasoning models across various objective questions, with F1 scores and accuracy surpassing other methods.
Zijun Liu, Peiyi Wang, Runxin Xu +5 authors
Self-Principled Critique Tuning enhances pointwise generative reward modeling for large language models, improving scalability and quality compared to existing methods.
Zichen Liu, Changyu Chen, Wenjun Li +5 authors
A study of R1-Zero training identifies pretraining effects on RL performance and proffers Dr. GRPO to enhance token efficiency, achieving superior accuracy on AIME 2024.
Nuo Chen, Zhiyuan Hu, Qingyun Zou +4 authors
JudgeLRM models trained with reinforcement learning outperform existing models, especially in tasks requiring deep reasoning.
Chris, Yichen Wei, Yi Peng +10 authors
Skywork R1V2 enhances multimodal reasoning through a hybrid reinforcement learning approach that balances reward-model guidance and rule-based strategies, improving training efficiency with the Selective Sample Buffer mechanism and mitigating visual hallucinations.
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