# Open-Reasoner-Zero/Open-Reasoner-Zero

Official Repo for Open-Reasoner-Zero

Repository: https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero
Canonical: https://ross.abutalabs.com/products/open-reasoner-zero
Homepage: https://yasminezhang.notion.site/Open-Reasoner-Zero-19e12cf72d418007b9cdebf44b0e7903
Language: Python
License: MIT
License Family: permissive
Last push: 2025-06-02T16:36:00+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 24, release rhythm 35, longevity 40
- inputs: {"age_days": 560, "days_push": 457, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2099, forks 120 (observed 2026-08-28T04:06:13.486838+00:00)

## What it is
Open-Reasoner-Zero is an open-source implementation of large-scale reinforcement learning training for reasoning-oriented language models, released with code, hyperparameters, training data, and model weights. It reproduces and improves upon DeepSeek-R1-Zero-style RL training, achieving strong benchmark results with fewer training steps.

## Use cases
- train a reasoning LLM with reinforcement learning from a base model
- reproduce DeepSeek-R1-Zero style RL training pipeline
- scale up RL training across model sizes from 0.5B to 32B
- improve math reasoning benchmarks like AIME2024 and MATH500
- research minimalist RL recipes for LLM reasoning
- download pretrained reasoning model weights for fine-tuning

## When to choose
- you want an open, reproducible RL training pipeline for reasoning models
- you have GPU cluster resources to train or fine-tune large language models
- you need training data, hyperparameters, and weights released together for research
- you want to study scalability of RL on base models

## When to avoid
- you only need inference of a reasoning model without training
- you lack multi-GPU infrastructure for large-scale RL training
- you need a general-purpose RL library unrelated to LLMs
- you want a plug-and-play chat model with no training involved

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, llm-training, machine-learning
- domain: reinforcement-learning, large-language-models, deep-learning, machine-learning
- platform: python
- tags: reasoning, rl-training, llm, open-source-research, deepseek-r1, model-training, gpu, linux

## Member repositories
- Open-Reasoner-Zero/Open-Reasoner-Zero (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.486838+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 2026-08-30T02:54:21.956521+00:00, confidence not recorded.
  - readme: https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero (fetched 2026-08-28T04:06:13.486838+00:00, sha a603172e5b6f)
  - homepage: https://yasminezhang.notion.site/Open-Reasoner-Zero-19e12cf72d418007b9cdebf44b0e7903 (fetched 2026-08-29T10:34:46.106676+00:00, sha 73a6ba54b760)
- Data as of 2026-08-30T08:39:29.467469+00:00.