REPOGEO REPORT · LITE
LiyuanLucasLiu/RAdam
Default branch master · commit d9fd30a3 · scanned 5/10/2026, 8:13:04 PM
GitHub: 2,549 stars · 332 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 LiyuanLucasLiu/RAdam, 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 value proposition statement to the README introduction
Why:
CURRENTWe are in an early-release beta. Expect some adventures and rough edges.
COPY-PASTE FIXInsert this sentence immediately after the H5: "RAdam is a theoretically sound variant of Adam that addresses the large variance of adaptive learning rates in early training, improving stability and often removing the need for learning rate warmup." Then, move the "We are in an early-release beta..." sentence to a new "Status" section or further down the README.
- mediumabout#2Update the repository description to be more explicit about RAdam's role
Why:
CURRENTOn the Variance of the Adaptive Learning Rate and Beyond
COPY-PASTE FIXRAdam: A theoretically sound variant of Adam that rectifies adaptive learning rate variance for more stable deep learning training.
- lowtopics#3Expand repository topics with more specific keywords
Why:
CURRENTadam, adam-optimizer, optimizer, warmup
COPY-PASTE FIXadam, adam-optimizer, optimizer, warmup, rectified-adam, stable-training, deep-learning-optimizer, learning-rate
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.
- AdamW · recommended 2×
- PyTorch · recommended 1×
- TensorFlow/Keras · recommended 1×
- Hugging Face Transformers · recommended 1×
- RAdam (Rectified Adam) · recommended 1×
- CATEGORY QUERYWhy does Adam optimizer require warmup and how to stabilize training?you: not recommendedAI recommended (in order):
- PyTorch
- TensorFlow/Keras
- Hugging Face Transformers
- AdamW
AI recommended 4 alternatives but never named LiyuanLucasLiu/RAdam. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking an improved adaptive learning rate optimizer for more stable deep learning training.you: not recommendedAI recommended (in order):
- AdamW
- RAdam (Rectified Adam)
- Lookahead
- AdaBelief
- Lion (EvoLved Sign MOmentum)
- SGD with Momentum and Learning Rate Schedules
AI recommended 6 alternatives but never named LiyuanLucasLiu/RAdam. 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 LiyuanLucasLiu/RAdam?passAI named LiyuanLucasLiu/RAdam explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts LiyuanLucasLiu/RAdam in production, what risks or prerequisites should they evaluate first?passAI named LiyuanLucasLiu/RAdam 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 LiyuanLucasLiu/RAdam solve, and who is the primary audience?passAI named LiyuanLucasLiu/RAdam 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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LiyuanLucasLiu/RAdam — 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