tulerfeng/Video-R1

• hightopics#1

  Add relevant topics to the repository

  原因：

  复制粘贴的修复

  复制

  multimodal-llm, video-reasoning, reinforcement-learning, mllm, video-ai, deep-learning, generative-ai, large-language-models

  待办完成忽略
• highreadme#2

  Reposition the core problem statement in the README

  原因：

  当前

  # Video-R1: Reinforcing Video Reasoning in MLLMs
  
  [📖 Paper] [🤗 Video-R1-7B-model] [🤗 Video-R1-train-data] 
  [🤖 Video-R1-7B-model]  [🤖 Video-R1-train-data]
  
  ## 👀 About Video-R1
  
  Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based RL, we introduce Video-R1 as **the first work to *systematically* explore the R1 paradigm for eliciting video reasoning** within MLLMs.

  复制粘贴的修复

  复制

  # Video-R1: Reinforcing Video Reasoning in MLLMs
  
  Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based RL, we introduce Video-R1 as **the first work to *systematically* explore the R1 paradigm for eliciting video reasoning** within MLLMs.
  
  [📖 Paper] [🤗 Video-R1-7B-model] [🤗 Video-R1-train-data] 
  [🤖 Video-R1-7B-model]  [🤖 Video-R1-train-data]
  
  ## 👀 About Video-R1

  待办完成忽略
• mediumlicense#3

  Add a LICENSE file to clarify usage terms

  原因：

  复制粘贴的修复

  复制

  Create a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if applicable) in the repository root to explicitly state the terms of use.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What techniques exist for improving MLLM temporal and spatial reasoning on video data?

  你：未被推荐
  AI 推荐顺序：

  1. Video Swin Transformer
  2. Timesformer (TimeSFormer)
  3. ViViT (Vision Transformer for Video)
  4. I3D (Inflated 3D ConvNet)
  5. R(2+1)D (Residual 2+1D ConvNet)
  6. RAFT
  7. PWC-Net
  8. Temporal Segment Networks (TSN)
  9. VideoLSTM
  10. Temporal Relation Networks (TRN)
  11. YOLOv8
  12. DETR
  13. ByteTrack
  14. DeepSORT
  15. Grounding DINO
  16. RoIAlign (from Mask R-CNN)
  17. Flamingo
  18. Video-LLaMA
  19. InternVideo
  20. VideoMAE
  21. CLIP (Contrastive Language-Image Pre-training)

  AI 推荐了 21 个替代方案，却始终没点名 tulerfeng/Video-R1。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking reinforcement learning approaches to elicit advanced reasoning in multimodal models.

  你：未被推荐
  AI 推荐顺序：

  1. PPO (Proximal Policy Optimization)
  2. DPO (Direct Preference Optimization)
  3. Constitutional AI (CAI)
  4. AlphaGo/AlphaZero-style Search
  5. DreamerV3

  AI 推荐了 5 个替代方案，却始终没点名 tulerfeng/Video-R1。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  fail

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of tulerfeng/Video-R1?

  pass
  ✓AI 明确点名了 tulerfeng/Video-R1

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts tulerfeng/Video-R1 in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 tulerfeng/Video-R1

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo tulerfeng/Video-R1 solve, and who is the primary audience?

  pass
  ⚠AI 未点名 tulerfeng/Video-R1 —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？