Qwen-Image Technical Report
Chenfei Wu, Jiahao Li, Jingren Zhou +36 authors
Qwen-Image, an image generation model, advances text rendering and image editing through a comprehensive data pipeline, progressive training, and dual-encoding mechanism.
Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
SwanTale: Unified Multi-Speaker Speech and Audio Generation for Instruct and Zero-Shot Tasks
Yu Zhang, Ruiqi Li, Changhao Pan +3 authors
SwanTale is a multi-speaker expressive speech and audio generation model that supports both instruction-based and zero-shot synthesis with high-quality multi-modality outputs.
On Data Engineering for Scaling LLM Terminal Capabilities
Renjie Pi, Grace Lam, Mohammad Shoeybi +3 authors
Researchers developed a synthetic task generation pipeline and analyzed data strategies to improve terminal agent performance, creating a large-scale dataset and models that outperform larger counterparts on benchmark tests.
Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models
Xin Xu, Clive Bai, Kai Yang +7 authors
Composition-RL improves reasoning capabilities by automatically composing multiple problems into new verifiable questions for reinforcement learning training.
Scaling Agent Learning via Experience Synthesis
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15 authors
DreamGym is a unified framework that synthesizes diverse experiences for scalable online RL training, improving agent performance and reducing real-world interactions.
Skywork-R1V3 Technical Report
Wei Shen, Jiangbo Pei, Yi Peng +7 authors
Skywork-R1V3, an open-source vision-language model, enhances visual reasoning through a post-training reinforcement learning framework, achieving state-of-the-art performance on multimodal reasoning tasks.
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Omkar Thawakar, Dinura Dissanayake, Ketan More +12 authors
A framework for evaluating and improving step-by-step visual reasoning in large language models using a specialized benchmark and a novel multimodal model trained with curriculum learning.
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30 authors
LFM2, a family of compact foundation models, achieves high efficiency and performance on-device through hardware-in-the-loop architecture search and advanced training techniques, supporting various tasks including multimodal applications.
Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning
Kejian Zhu, Zhuoran Jin, Dongqi Huang +4 authors
Effective multimodal agent training is improved by selecting diverse environments via ability-aware selection and structuring difficulty through hierarchical curriculum learning.
PolyLM: An Open Source Polyglot Large Language Model
Xiangpeng Wei, Haoran Wei, Huan Lin +15 authors
PolyLM, a multilingual LLM trained on 640 billion tokens, enhances multilingual capabilities through bilingual data and curriculum learning, outperforming other models on multilingual tasks while maintaining English performance.
Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma, William Liang, Guanzhi Wang +6 authors
Eureka, an LLM-powered reward design algorithm, generates human-level reward functions enhancing reinforcement learning for complex manipulation tasks and RLHF, including pen spinning with a simulated Shadow Hand.
DiagEvo: Diagnosis-Guided Self-Evolution via Hierarchical Error Memory
Xincheng Wei, Yifan Ding, Yoshua Li +5 authors
DiagEvo improves language-model self-evolution by deriving training direction from internal failure history via hierarchical error-cause memory and double-confidence filtering, outperforming external-resource baselines.
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