Arxiv Papers

Igor Melnyk
  • 5.0 (3)
  • 科学
  • 一日一更

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers

  1. 1天前

    [QA] NABLA: Neighborhood Adaptive Block-Level Attention

    NABLA introduces a Neighborhood Adaptive Block-Level Attention mechanism for video diffusion transformers, enhancing efficiency and speed while maintaining quality, achieving up to 2.7 times faster training and inference. https://arxiv.org/abs//2507.13546 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    7 分钟

  2. 1天前

    NABLA: Neighborhood Adaptive Block-Level Attention

    NABLA introduces a Neighborhood Adaptive Block-Level Attention mechanism for video diffusion transformers, enhancing efficiency and speed while maintaining quality, achieving up to 2.7 times faster training and inference. https://arxiv.org/abs//2507.13546 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    13 分钟

  3. 1天前

    [QA] Checklists Are Better Than Reward Models For Aligning Language Models

    The paper introduces "Reinforcement Learning from Checklist Feedback" (RLCF), enhancing language model instruction-following by using flexible, instruction-specific criteria, outperforming traditional methods across multiple benchmarks. https://arxiv.org/abs//2507.18624 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    5 分钟

  4. 1天前

    Checklists Are Better Than Reward Models For Aligning Language Models

    The paper introduces "Reinforcement Learning from Checklist Feedback" (RLCF), enhancing language model instruction-following by using flexible, instruction-specific criteria, outperforming traditional methods across multiple benchmarks. https://arxiv.org/abs//2507.18624 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    14 分钟

  5. 2天前

    [QA] Beyond Binary Rewards: Training LMs to Reason about Their Uncertainty

    The paper introduces RLCR, a reinforcement learning approach that enhances language model accuracy and confidence calibration, improving performance on question answering tasks without sacrificing accuracy. https://arxiv.org/abs//2507.16806 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    8 分钟

  6. 2天前

    Beyond Binary Rewards: Training LMs to Reason about Their Uncertainty

    The paper introduces RLCR, a reinforcement learning approach that enhances language model accuracy and confidence calibration, improving performance on question answering tasks without sacrificing accuracy. https://arxiv.org/abs//2507.16806 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    15 分钟

  7. 2天前

    [QA] Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

    The paper introduces Rubrics as Rewards (RaR), a framework using structured rubrics for interpretable reward signals in reinforcement learning, improving performance and alignment with human preferences in real-world tasks. https://arxiv.org/abs//2507.17746 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    7 分钟

  8. 2天前

    Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

    The paper introduces Rubrics as Rewards (RaR), a framework using structured rubrics for interpretable reward signals in reinforcement learning, improving performance and alignment with human preferences in real-world tasks. https://arxiv.org/abs//2507.17746 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    12 分钟
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5
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关于

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers

信息

  • 创作者
    Igor Melnyk
  • 活跃年份
    2023年 - 2025年
  • 单集
    2000
  • 分级
    儿童适宜
  • 版权
    © Igor Melnyk
  • 节目网站
    Arxiv Papers

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