RRepoGEO

REPOGEO REPORT · LITE

Tencent-Hunyuan/MixGRPO

Default branch main · commit e7f299d4 · scanned 5/22/2026, 11:13:22 PM

GitHub: 1,137 stars · 49 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /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
3 / 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/MixGRPO, 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
  • highreadme#1
    Add a concise problem/solution statement to the README's opening

    Why:

    COPY-PASTE FIX
    MixGRPO significantly enhances the efficiency of flow-based GRPO in reinforcement learning by integrating a novel mixed ODE-SDE framework for policy optimization.
  • mediumtopics#2
    Expand repository topics to include specific technical methods

    Why:

    CURRENT
    diffusion, grpo, reinforcement-learning
    COPY-PASTE FIX
    reinforcement-learning, policy-optimization, flow-based-models, ode-sde, grpo, stochastic-processes
  • lowlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms specified in the LICENSE file. Please refer to the LICENSE file for full details on the applicable license(s).

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/MixGRPO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Probability
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Probability · recommended 1×
  2. PyTorch Distributions · recommended 1×
  3. nflows · recommended 1×
  4. FrEIA (Framework for Invertible Architectures) · recommended 1×
  5. VIME (Variational Information Maximizing Exploration) · recommended 1×
  • CATEGORY QUERY
    How can I achieve more efficient policy optimization in reinforcement learning using flow-based models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Probability
    2. PyTorch Distributions
    3. nflows
    4. FrEIA (Framework for Invertible Architectures)
    5. VIME (Variational Information Maximizing Exploration)

    AI recommended 5 alternatives but never named Tencent-Hunyuan/MixGRPO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for reinforcement learning frameworks that integrate mixed ordinary and stochastic differential equations.
    you: not recommended
    AI recommended (in order):
    1. Diffrax.jl (JuliaDiffEq/Diffrax.jl)
    2. ReinforcementLearning.jl (JuliaReinforcementLearning/ReinforcementLearning.jl)
    3. PyTorch-SDE (google-research/torchsde)
    4. RLlib (ray-project/ray)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. PyTorch-Lightning-RL
    7. JAX-SDE (google-research/jax-sde)
    8. RLax (deepmind/rlax)
    9. Acme (deepmind/acme)
    10. JuliaDiffEq
    11. TensorFlow Probability (tensorflow/probability)
    12. TensorFlow Agents (TF-Agents) (tensorflow/agents)

    AI recommended 12 alternatives but never named Tencent-Hunyuan/MixGRPO. 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/MixGRPO?
    pass
    AI named Tencent-Hunyuan/MixGRPO 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/MixGRPO in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tencent-Hunyuan/MixGRPO 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/MixGRPO solve, and who is the primary audience?
    pass
    AI named Tencent-Hunyuan/MixGRPO explicitly

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

Embed your GEO score

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Tencent-Hunyuan/MixGRPO — 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