REPOGEO REPORT · LITE
PRIME-RL/TTRL
Default branch main · commit 5806e119 · scanned 5/14/2026, 4:58:18 PM
GitHub: 1,072 stars · 83 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/TTRL, 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.
- highreadme#1Add a clear statement linking TTRL to LLM reasoning and inference-time adaptation in the README's introduction
Why:
COPY-PASTE FIXAdd this sentence immediately after the main H1 title: 'TTRL provides a novel framework for enhancing large language model reasoning performance and adapting LLMs during inference using reinforcement learning.'
- mediumtopics#2Add more specific topics to highlight test-time and inference-time LLM adaptation
Why:
CURRENTllm, reasoning, rl
COPY-PASTE FIXllm, reasoning, rl, test-time-adaptation, inference-time-rl, llm-adaptation
- lowabout#3Expand the repository description to explicitly mention LLM applications
Why:
CURRENT[NeurIPS 2025] TTRL: Test-Time Reinforcement Learning
COPY-PASTE FIX[NeurIPS 2025] TTRL: Test-Time Reinforcement Learning for enhancing LLM reasoning and adapting models during inference.
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.
- huggingface/peft · recommended 2×
- huggingface/transformers · recommended 1×
- CATEGORY QUERYHow can I enhance large language model reasoning performance using reinforcement learning techniques?you: not recommended
Show full AI answer
- CATEGORY QUERYSeeking methods to adapt LLMs with reinforcement learning during inference for better accuracy.you: not recommendedAI recommended (in order):
- Hugging Face Transformers (huggingface/transformers)
- PEFT (huggingface/peft)
- LoRA (huggingface/peft)
AI recommended 3 alternatives but never named PRIME-RL/TTRL. 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/TTRL?passAI named PRIME-RL/TTRL 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/TTRL in production, what risks or prerequisites should they evaluate first?passAI named PRIME-RL/TTRL 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/TTRL solve, and who is the primary audience?passAI named PRIME-RL/TTRL explicitly
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 PRIME-RL/TTRL. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/PRIME-RL/TTRL)<a href="https://repogeo.com/en/r/PRIME-RL/TTRL"><img src="https://repogeo.com/badge/PRIME-RL/TTRL.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
PRIME-RL/TTRL — 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