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  • Qwen-VL: A Versatile Vision-Language Model for Understanding. . .
    In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images Starting from the Qwen-LM as a foundation, we endow it with visual capacity by the meticulously designed (i) visual receptor, (ii) input-output interface, (iii) 3-stage training pipeline, and (iv) multilingual multimodal cleaned corpus
  • Junyang Lin - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND QWEN-VL: A . . .
    In this paper, we explore a way out and present the newest members of the open-sourced Qwen fam-ilies: Qwen-VL series Qwen-VLs are a series of highly performant and versatile vision-language foundation models based on Qwen-7B (Qwen, 2023) language model We empower the LLM base-ment with visual capacity by introducing a new visual receptor including a language-aligned visual encoder and a
  • Long-Text-to-Image Generation via Compositional Prompt Decomposition
    Given the emergence of Flux, Qwen-Image, and similar models, exploring complex prompt generation on these newer architectures would be more valuable How should this method be adapted to state-of-the-art models like Qwen-Image (with Qwen2 5-VL as encoder) or MetaQuery-type architectures? What modifications are necessary for effective transfer?
  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • Scaling Behaviors of LLM Reinforcement Learning Post-Training: An . . .
    While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post- training remains largely unexplored This paper presents a systematic empirical investigation of scaling behaviors in RL-based post-training, with a particular fo- cus on mathematical reasoning Based on a set of experiments across the
  • Zihan Qiu - OpenReview
    Career Education History Researcher Qwen Team, Alibaba Group (alibaba-inc com) 2024 – Present Undergrad student IIIS, Tsinghua University, Tsinghua University (tsinghua edu cn)
  • Mamba-3: Improved Sequence Modeling using State Space Principles
    This submission introduces Mamba-3, an “inference-first” state-space linear-time sequence model that aims to improve over prior sub-quadratic backbones (notably Mamba-2 and Gated DeltaNet) along three dimensions: modeling quality, state-tracking capability, and real-world decode efficiency The core methodological contributions are: Generalized trapezoidal discretization to improve
  • You Know What Im Saying: Jailbreak Attack via Implicit Reference
    Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding $\textbf {90}$% on most models, including GPT-4o, Claude-3 5-Sonnet, and Qwen-2-72B Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method
  • J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement. . .
    In particular, J1-Qwen-32B, our multitasked pointwise and pairwise judge also outperforms o1-mini, o3, and a much larger 671B DeepSeek-R1 on some benchmarks, while only training on synthetic data





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