2 citations · 2 across the 13 of their papers we have counts for
9 papers · 1 filter
Qwen-Image-2.0-RL Technical Report
Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu…
Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation
Niantong Li, Guangzheng Hu, Weixu Qiao +35
Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jie Zhang, Xiaoyue Chen, Anzhe Chen +36
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…
Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Tianhe Wu, Kun Yan, Zikai Zhou +23
Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially reducing inference overhead while enabli…
Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving
Chunan Shi, Yilei Chen, Yilin Chen +2
Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation. While approximate KV cache retention techniques reduce memory usage b…
Qwen-Image-VAE-2.0 Technical Report
Zekai Zhang, Deqing Li, Kuan Cao +27
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To a…
Qwen-Image-2.0 Technical Report
Bing Zhao, Chenfei Wu, Deqing Li +72
We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…
When Less is Enough: Efficient Inference via Collaborative Reasoning
Yilei Chen, Sharut Gupta, Yannis Paschalidis +2
In this work, we introduce DUET (Dual-model Efficient Two-stage inference), a collaborative inference framework in which a capable model and a lightweight model work together to so…
Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL
Xiaofeng Lin, Sirou Zhu, Yilei Chen +6
Large language models (LLMs) achieve strong performance when all task-relevant information is available upfront, as in static prediction and instruction-following problems. However…