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英文字典中文字典相关资料:


  • Junyang Lin - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • 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
  • Kai Dang - OpenReview
    Career Education History Researcher Qwen team, Alibaba Group (alibaba-inc com) 2022 – 2025 MS student Nankai University (nankai edu cn)
  • Variational Reasoning for Language Models | OpenReview
    We empirically validate our method on the Qwen 2 5 and Qwen 3 model families across a wide range of reasoning tasks Overall, our work provides a principled probabilistic perspective that unifies variational inference with RL-style methods and yields stable objectives for improving the reasoning ability of language models Supplementary
  • Gated Attention for Large Language Models: Non-linearity, Sparsity,. . .
    The authors response that they will add experiments in QWen architecture, give the hyperparameters, and promise to open-source one of the models Reviewer bMKL is the only reviewer to initially score the paper in the negative region (Borderline reject) They have some doubts on the experimental section
  • 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
  • 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
  • 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)
  • Towards Federated RLHF with Aggregated Client Preference for LLMs
    For example, our experiments demonstrate that the Qwen-2-0 5B selector provides strong performance enhancements to larger base models like Gemma-2B while ensuring computationally efficient This approach reduces the training burden for federated RLHF and broadens its applicability to resource-constrained scenarios
  • Cache-to-Cache: Direct Semantic Communication Between Large Language . . .
    Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains that are not attainable by a single model In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences This process both loses rich semantic information and incurs token-by-token generation





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