REPOGEO REPORT · LITE
mbzuai-oryx/Awesome-LLM-Post-training
Default branch main · commit a9e3e1cc · scanned 5/22/2026, 10:18:12 AM
GitHub: 2,416 stars · 161 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 mbzuai-oryx/Awesome-LLM-Post-training, 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#1Reposition README H1 and opening paragraph to clarify repo's nature as a curated list
Why:
CURRENT# LLM Post-Training: A Deep Dive into Reasoning Large Language Models Welcome to the **Awesome-LLM-Post-training** repository! This repository is a curated collection of the most influential papers, code implementations, benchmarks, and resources related to **Large Language Models (LLMs) Post-Training Methodologies**.
COPY-PASTE FIX# Awesome-LLM-Post-training: A Curated Collection of Resources for Enhancing LLM Reasoning Welcome to the **Awesome-LLM-Post-training** repository! This is a comprehensive, curated collection of the most influential papers, code implementations, benchmarks, and resources specifically focused on **Large Language Models (LLMs) Post-Training Methodologies** and enhancing their reasoning capabilities. Unlike standalone libraries, frameworks, or datasets, this repository serves as a central guide and survey of the field.
- highlicense#2Add a LICENSE file to the repository
Why:
CURRENT(no LICENSE file detected — the repo has no recognizable license)
COPY-PASTE FIXCreate a LICENSE file (e.g., MIT, as implied by the README excerpt) in the repository root.
- mediumtopics#3Add more specific topics to reflect the repo's nature as a curated list/survey
Why:
CURRENTfine, large-language-models, post-training, reasoning, reinforcement-learning, scaling
COPY-PASTE FIXfine-tuning, large-language-models, post-training, reasoning, reinforcement-learning, scaling, awesome-list, survey, llm-resources, guide
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.
- GSM8K · recommended 1×
- MATH Dataset · recommended 1×
- Big-Bench Hard (BBH) · recommended 1×
- TuningFork · recommended 1×
- huggingface/trl · recommended 1×
- CATEGORY QUERYHow can I enhance the reasoning abilities of my large language models post-initial training?you: not recommendedAI recommended (in order):
- GSM8K
- MATH Dataset
- Big-Bench Hard (BBH)
- TuningFork
- TRL (Transformer Reinforcement Learning) (huggingface/trl)
- Neo4j (neo4j/neo4j)
- Wikidata
- Grakn (now Vaticle's TypeDB) (vaticle/typedb)
- LangChain (langchain-ai/langchain)
- LlamaIndex (run-llama/llama_index)
- Hugging Face Agents (huggingface/transformers)
AI recommended 11 alternatives but never named mbzuai-oryx/Awesome-LLM-Post-training. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are effective post-training methods for improving LLM performance and reasoning capabilities?you: not recommendedAI recommended (in order):
- Hugging Face Transformers
- LoRA
- QLoRA
- Hugging Face TRL
- DeepSpeed-Chat
- PPO
- LangChain
- LlamaIndex
- OpenAI API
- Anthropic API
- Faiss (Facebook AI Similarity Search)
- Chroma
- Pinecone
- Weaviate
- TinyLlama
- DistilBERT
AI recommended 16 alternatives but never named mbzuai-oryx/Awesome-LLM-Post-training. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- 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 mbzuai-oryx/Awesome-LLM-Post-training?passAI did not name mbzuai-oryx/Awesome-LLM-Post-training — 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?
- If a team adopts mbzuai-oryx/Awesome-LLM-Post-training in production, what risks or prerequisites should they evaluate first?passAI named mbzuai-oryx/Awesome-LLM-Post-training 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 mbzuai-oryx/Awesome-LLM-Post-training solve, and who is the primary audience?passAI did not name mbzuai-oryx/Awesome-LLM-Post-training — 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
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mbzuai-oryx/Awesome-LLM-Post-training — 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