# mbzuai-oryx/Awesome-LLM-Post-training

Awesome Reasoning LLM Tutorial/Survey/Guide

Repository: https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
Canonical: https://ross.abutalabs.com/products/awesome-llm-post-training
Language: Python
License Family: other
Topics: fine, large-language-models, post-training, reasoning, reinforcement-learning, scaling
Last push: 2026-07-27T13:26:23+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 35, longevity 40
- inputs: {"age_days": 566, "days_push": 37, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2526, forks 167 (observed 2026-08-28T04:06:58.545358+00:00)

## What it is
A curated awesome-list and survey companion collecting papers, code, benchmarks, and resources on LLM post-training methodologies, organized into fine-tuning, reinforcement learning, and test-time scaling. It accompanies a TPAMI-accepted survey paper on reasoning large language models.

## Use cases
- find papers on LLM post-training techniques
- learn about reasoning LLM methods like RLHF and test-time scaling
- survey fine-tuning and reinforcement learning approaches for LLMs
- find benchmarks for reasoning large language models
- keep up with new research on LLM reasoning
- prepare a literature review on post-training LLMs

## When to choose
- you need a curated, taxonomy-organized reading list on LLM post-training
- you are researching reasoning models, RLHF, or test-time scaling
- you want the companion resources for the TPAMI/arXiv survey paper

## When to avoid
- you need runnable training code or a production training framework
- you want a hands-on tutorial with step-by-step exercises rather than a paper list
- you need a maintained software library with releases

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, machine-learning, documentation
- domain: large-language-models, artificial-intelligence, tutorials, awesome-lists, reinforcement-learning
- platform: python
- tags: post-training, reasoning, fine-tuning, reinforcement-learning, test-time-scaling, survey, curated-list, rlhf

## Member repositories
- mbzuai-oryx/Awesome-LLM-Post-training (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:58.545358+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:25:29.084874+00:00, confidence not recorded.
  - readme: https://github.com/mbzuai-oryx/Awesome-LLM-Post-training (fetched 2026-08-28T04:06:58.545358+00:00, sha 4216892aab44)
- Data as of 2026-08-30T08:39:29.467469+00:00.