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TsinghuaC3I/Awesome-RL-for-LRMs
默认分支 main · commit ecc8ba08 · 扫描时间 2026/5/22 12:57:13
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下方为分数趋势(含全部就绪扫描;左旧右新,可横向滚动)。表格明细默认折叠,展开后每页 10 条,最新在上。
共 2 条就绪扫描。点击下方按钮展开表格(每页 10 条,可翻页)。
行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 TsinghuaC3I/Awesome-RL-for-LRMs 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。
行动计划 — 可复制粘贴的修复
3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。
- highreadme#1Add an explicit introductory sentence to the README clarifying the repo's nature
原因:
当前The README starts with a title and a general quote.
复制粘贴的修复Add a clear introductory sentence immediately after the main title, e.g., 'This repository serves as a comprehensive, curated collection of research papers and resources on Reinforcement Learning for Large Reasoning Models, designed for researchers and practitioners. It is not a deployable software package or library.'
- mediumtopics#2Expand repository topics to include survey and research-related terms
原因:
当前awesome-list, deepseek-r1, llm, lrm, open-source, reasoning, rl
复制粘贴的修复awesome-list, deepseek-r1, llm, lrm, open-source, reasoning, rl, survey, research, literature-review, papers, resource-collection
- mediumreadme#3Reorganize README to prioritize the survey's value proposition over news
原因:
当前The 'News' section appears immediately after the title and an introductory quote.
复制粘贴的修复Introduce a dedicated 'About This Survey' or 'Introduction' section immediately after the initial clarifying sentence, detailing the survey's scope and structure, and place the 'News' section below it.
本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash
品类可见性 — 真正的 GEO 测试
向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?
各模型使用同一组问题 — 切换标签对比回答与排名。
- InstructGPT · 被推荐 1 次
- ChatGPT · 被推荐 1 次
- Hugging Face Transformers · 被推荐 1 次
- Triton · 被推荐 1 次
- PyTorch · 被推荐 1 次
- 品类问题How can I improve reasoning capabilities of large language models using reinforcement learning?你:未被推荐AI 推荐顺序:
- InstructGPT
- ChatGPT
- Hugging Face Transformers
- Triton
- PyTorch
- TensorFlow
- Claude
- Reflexion
- Self-Correction LLMs
- Toolformer
- Gorilla
- AutoGPT
- BabyAGI
- RLlib
- CoT-Llama
- PAL
AI 推荐了 16 个替代方案,却始终没点名 TsinghuaC3I/Awesome-RL-for-LRMs。这就是要补上的差距。
查看 AI 完整回答
- 品类问题Where can I find a comprehensive survey on reinforcement learning techniques for large AI reasoning models?你:未被推荐AI 推荐顺序:
- Reinforcement Learning for Large Language Models: A Survey
- A Survey on Reinforcement Learning from Human Feedback
- A Survey of Large Language Models
- Harnessing the Power of LLMs: A Survey of Techniques and Applications
- Deep Reinforcement Learning: An Overview by Li
- Reinforcement Learning for Natural Language Processing: A Survey
AI 推荐了 6 个替代方案,却始终没点名 TsinghuaC3I/Awesome-RL-for-LRMs。这就是要补上的差距。
查看 AI 完整回答
客观检查
针对 AI 引擎最看重的元数据信号的规则审计。
- Metadata completenesspass
- README presencepass
自指检查
当被直接问到你时,AI 是否还知道你的仓库存在?
- Compared to common alternatives in this category, what is the core differentiator of TsinghuaC3I/Awesome-RL-for-LRMs?passAI 未点名 TsinghuaC3I/Awesome-RL-for-LRMs —— 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?
- If a team adopts TsinghuaC3I/Awesome-RL-for-LRMs in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 TsinghuaC3I/Awesome-RL-for-LRMs
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 TsinghuaC3I/Awesome-RL-for-LRMs solve, and who is the primary audience?passAI 未点名 TsinghuaC3I/Awesome-RL-for-LRMs —— 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?
嵌入你的 GEO 徽章
把这个徽章贴进 TsinghuaC3I/Awesome-RL-for-LRMs 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。
[](https://repogeo.com/zh/r/TsinghuaC3I/Awesome-RL-for-LRMs)<a href="https://repogeo.com/zh/r/TsinghuaC3I/Awesome-RL-for-LRMs"><img src="https://repogeo.com/badge/TsinghuaC3I/Awesome-RL-for-LRMs.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
TsinghuaC3I/Awesome-RL-for-LRMs — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。
- 深度报告每月 10 次
- 无品牌品类查询5,轻量 2
- 优先行动项8,轻量 3