RRepoGEO

REPOGEO REPORT · LITE

Tencent-Hunyuan/UniRL

Default branch main · commit cdbb076f · scanned 6/17/2026, 4:33:56 AM

GitHub: 625 stars · 34 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Tencent-Hunyuan/UniRL, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Expand repository topics to include multimodal and unified model specifics

    Why:

    CURRENT
    ai-infrastructure, reinforcement-learning, sglang, vllm
    COPY-PASTE FIX
    ai-infrastructure, reinforcement-learning, multimodal-ai, unified-models, large-language-models, diffusion-models, ar-models, llm-rl
  • highreadme#2
    Add a concise problem-solution statement to the top of the README

    Why:

    CURRENT
    The first prose after the title/tagline is the 'News' section, followed by 'About 💡'.
    COPY-PASTE FIX
    Add the following sentence immediately after the tagline `**U**(you)·**ni**(need)·**RL** for unified multimodal intelligence`:
    `UniRL addresses the complexity of applying reinforcement learning to diverse multimodal models by providing a unified framework that streamlines the entire RL post-training loop across model families like diffusion and autoregressive models.`
  • mediumlicense#3
    Clarify the license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example:
    `## License`
    `UniRL is released under [specify actual license(s) here, e.g., "a custom license combining Apache-2.0 and MIT terms"]. Please see the [LICENSE](LICENSE) file for full details.`

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Tencent-Hunyuan/UniRL
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. TRL (Transformer Reinforcement Learning) · recommended 1×
  3. DeepMind's Acme · recommended 1×
  4. OpenAI's Baselines · recommended 1×
  5. Stable Baselines3 · recommended 1×
  • CATEGORY QUERY
    How can I apply reinforcement learning to improve unified multimodal AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL (Transformer Reinforcement Learning)
    3. DeepMind's Acme
    4. OpenAI's Baselines
    5. Stable Baselines3
    6. RLlib
    7. TorchRL
    8. TensorFlow Agents (TF-Agents)
    9. PyTorch-Lightning
    10. PyTorch
    11. TensorFlow

    AI recommended 11 alternatives but never named Tencent-Hunyuan/UniRL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an RL framework to train various multimodal models like diffusion and AR.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (Transformer Reinforcement Learning) (huggingface/trl)
    3. RLlib (Ray RLlib) (ray-project/ray)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. DeepMind's Acme (deepmind/acme)
    6. PyTorch Lightning (Lightning-AI/lightning)
    7. TensorFlow Agents (TF-Agents) (tensorflow/agents)

    AI recommended 7 alternatives but never named Tencent-Hunyuan/UniRL. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Tencent-Hunyuan/UniRL?
    pass
    AI named Tencent-Hunyuan/UniRL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Tencent-Hunyuan/UniRL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tencent-Hunyuan/UniRL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Tencent-Hunyuan/UniRL solve, and who is the primary audience?
    pass
    AI did not name Tencent-Hunyuan/UniRL — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of Tencent-Hunyuan/UniRL. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Tencent-Hunyuan/UniRL.svg)](https://repogeo.com/en/r/Tencent-Hunyuan/UniRL)
HTML
<a href="https://repogeo.com/en/r/Tencent-Hunyuan/UniRL"><img src="https://repogeo.com/badge/Tencent-Hunyuan/UniRL.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Tencent-Hunyuan/UniRL — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite