REPOGEO REPORT · LITE
PRIME-RL/SimpleVLA-RL
Default branch main · commit 7c51662d · scanned 5/12/2026, 9:38:00 PM
GitHub: 1,645 stars · 106 forks
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 PRIME-RL/SimpleVLA-RL, 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.
- highabout#1Expand the repository description to clarify "VLA"
Why:
CURRENT[ICLR 2026] SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
COPY-PASTE FIX[ICLR 2026] SimpleVLA-RL: An efficient Reinforcement Learning (RL) framework for Vision-Language-Action (VLA) models, designed to improve long-horizon planning in robotics under data scarcity.
- highreadme#2Add an explicit definition of VLA to the README's opening paragraph
Why:
CURRENT**SimpleVLA-RL** is an efficient RL framework for VLA that improves long-horizon planning under data scarcity.
COPY-PASTE FIX**SimpleVLA-RL** is an efficient Reinforcement Learning (RL) framework specifically designed for Vision-Language-Action (VLA) models, which are crucial for improving long-horizon planning in robotics, especially under data scarcity.
- mediumtopics#3Add specific topics for robotics and manipulation
Why:
CURRENTreasoning, rl, vla
COPY-PASTE FIXreasoning, rl, vla, robotics, manipulation, vision-language-action
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.
- Diffuser · recommended 1×
- ActiDiff · recommended 1×
- CLIP · recommended 1×
- ALIGN · recommended 1×
- OpenFlamingo · recommended 1×
- CATEGORY QUERYHow can I improve long-horizon planning for vision-language-action models with limited data?you: not recommendedAI recommended (in order):
- Diffuser
- ActiDiff
- CLIP
- ALIGN
- OpenFlamingo
- HIRO
- Feudal Networks
- Decision Transformer
- GATO
- RandAugment
- CutMix
- CQL
- IQL
- TD3+BC
- DIAYN
- SMM
AI recommended 16 alternatives but never named PRIME-RL/SimpleVLA-RL. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat reinforcement learning frameworks are best for real-world VLA tasks, outperforming supervised fine-tuning?you: not recommendedAI recommended (in order):
- RLlib (ray-project/ray)
- Acme (deepmind/acme)
- Stable Baselines3 (DLR-RM/stable-baselines3)
- TorchRL (pytorch/rl)
- Tianshou (thu-ml/tianshou)
AI recommended 5 alternatives but never named PRIME-RL/SimpleVLA-RL. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
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 PRIME-RL/SimpleVLA-RL?passAI named PRIME-RL/SimpleVLA-RL explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts PRIME-RL/SimpleVLA-RL in production, what risks or prerequisites should they evaluate first?passAI named PRIME-RL/SimpleVLA-RL 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 PRIME-RL/SimpleVLA-RL solve, and who is the primary audience?passAI did not name PRIME-RL/SimpleVLA-RL — 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?
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PRIME-RL/SimpleVLA-RL — 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