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Thaddäus Wiedemer, Yuxuan Li, Paul Vicol +6 authors
Veo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.
JiYuan Wang, Chunyu Lin, Lei Sun +6 authors
FE2E, a framework using a Diffusion Transformer for dense geometry prediction, outperforms generative models in zero-shot monocular depth and normal estimation with improved performance and efficiency.
Kuan Li, Zhongwang Zhang, Huifeng Yin +14 authors
WebSailor, a post-training methodology, enhances open-source models with systematic uncertainty reduction, matching proprietary agents' performance in complex information-seeking tasks.
Yuxiang Ji, Ziyu Ma, Yong Wang +3 authors
Tree-based Group Relative Policy Optimization (Tree-GRPO) enhances reinforcement learning for large language models by using tree search to improve rollouts and estimate grouped relative advantages, outperforming chain-based methods.
Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem
Hala, a family of Arabic-centric instruction and translation models, achieves state-of-the-art results using a translate-and-tune pipeline, slerp merging, and fine-tuning on high-quality bilingual supervision.
Team Seedream, Yunpeng Chen, Yu Gao +47 authors
Seedream 4.0 is a high-performance multimodal image generation system that integrates text-to-image synthesis, image editing, and multi-image composition using a diffusion transformer and VAE, achieving state-of-the-art results with efficient training and inference.
Xiyao Wang, Chunyuan Li, Jianwei Yang +4 authors
Reinforcement learning on preference-labeled critic datasets enhances a generative model's performance, creating a unified multimodal system that excels in both evaluation and generation.
Heeji Yoon, Jaewoo Jung, Junwan Kim +10 authors
VIRAL, a regularization strategy, aligns MLLMs' visual representations with pre-trained VFMs, enhancing performance on vision-centric tasks.
Zhenghai Xue, Longtao Zheng, Qian Liu +4 authors
SimpleTIR stabilizes multi-turn Tool-Integrated Reasoning training by filtering out void turns, achieving state-of-the-art performance on math reasoning benchmarks.
Junteng Liu, Yunji Li, Chi Zhang +12 authors
WebExplorer, a data-driven approach for developing advanced web agents, achieves state-of-the-art performance in information-seeking tasks through systematic data generation and reinforcement learning.
Haozhan Li, Yuxin Zuo, Jiale Yu +18 authors
SimpleVLA-RL, an RL framework for VLA models, enhances long-horizon action planning, achieves state-of-the-art performance, and discovers novel patterns during training.
Dongfu Jiang, Yi Lu, Zhuofeng Li +9 authors
VerlTool is a unified and modular framework for Agentic Reinforcement Learning with Tool use, addressing inefficiencies in existing approaches and providing competitive performance across multiple domains.
Xixi Wu, Kuan Li, Yida Zhao +11 authors
ReSum, a novel paradigm with periodic context summarization, enhances web agents' performance on knowledge-intensive tasks by overcoming context window limitations, achieving significant improvements over ReAct.
Delin Qu, Haoming Song, Qizhi Chen +12 authors
EO-Robotics, comprising EO-1 model and EO-Data1.5M dataset, advances multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training.
Xingtai Lv, Yuxin Zuo, Youbang Sun +9 authors
A unified policy gradient estimator and Hybrid Post-Training algorithm effectively combine online and offline data for post-training language models, improving performance across various benchmarks.
Xiaochuan Li, Guoguang Du, Runze Zhang +11 authors
Using video data to provide commonsense priors enhances 3D asset generation, enabling spatial consistency and semantic plausibility in 3D content creation.
Ziyi Xia, Kun Luo, Hongjin Qian +1 authors
InfoSeek is a scalable framework for generating complex Deep Research tasks by synthesizing hierarchical constraint satisfaction problems, enabling models to outperform larger baselines on challenging benchmarks.
Runnan Fang, Shihao Cai, Baixuan Li +13 authors
A scalable framework and two-phase fine-tuning strategy enhance function-calling capabilities of agents in diverse environments, improving performance on agentic benchmarks.
Jie Wu, Yu Gao, Zilyu Ye +9 authors
RewardDance is a scalable reward modeling framework that aligns with VLM architectures, enabling effective scaling of RMs and resolving reward hacking issues in generation models.
Xiangxin Zhou, Zichen Liu, Haonan Wang +5 authors
A variational reasoning framework treats thinking traces as latent variables, optimizing them through variational inference to improve language model reasoning.
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