# RLinf/RLinf

RLinf: Reinforcement Learning Infrastructure for Embodied and Agentic AI

Repository: https://github.com/RLinf/RLinf
Canonical: https://ross.abutalabs.com/products/rlinf
Homepage: https://rlinf.readthedocs.io/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: reinforcement-learning, agentic-ai, rlinf, vla-rl, embodied-ai, rl-infra
Last push: 2026-08-26T12:20:04+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 69, longevity 27
- inputs: {"age_days": 384, "days_push": 7, "days_rel": 49, "gap_med": 104.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4655, forks 685 (observed 2026-08-28T04:08:55.658464+00:00)

## What it is
RLinf is an open-source, flexible and scalable reinforcement learning training infrastructure for embodied AI (vision-language-action models) and agentic AI. It supports distributed RL training workflows like GRPO across multiple GPU accelerators including NVIDIA, AMD ROCm, Huawei Ascend, and Moore Threads.

## Use cases
- train vision-language-action models with reinforcement learning
- run GRPO training for large language models
- RL training for robot manipulation policies on LIBERO
- reinforcement learning for video generation models
- train agentic AI models with RL post-training
- scalable distributed RL infrastructure for embodied AI research

## When to choose
- you need a scalable RL training backbone for VLA or agentic models
- you want multi-vendor accelerator support (NVIDIA, AMD, Ascend, MUSA)
- you need GRPO-style post-training for LLMs or diffusion models
- you want an RL infrastructure officially integrated with Isaac Lab

## When to avoid
- you only need simple single-GPU RL experiments with lightweight libraries like Gym or Stable-Baselines3
- you need supervised fine-tuning only without reinforcement learning
- you work outside Python-based ML training pipelines

## Facets
- artifact type: framework
- maturity: active
- function: reinforcement-learning, llm-training, machine-learning, gpu-computing, workflow-automation
- domain: reinforcement-learning, machine-learning, robotics, artificial-intelligence, gpu-computing
- platform: python
- tags: rl-infrastructure, embodied-ai, agentic-ai, vla-training, grpo, rlhf, vision-language-action, distributed-training, robot-learning, video-generation, gpu, linux, docker

## Member repositories
- RLinf/RLinf (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:55.658464+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 2026-08-29T18:19:28.661348+00:00, confidence not recorded.
  - readme: https://github.com/RLinf/RLinf (fetched 2026-08-28T04:08:55.658464+00:00, sha 8fd0ca5ece5a)
  - registry_pypi: https://pypi.org/pypi/rlinf/json (fetched 2026-08-29T09:04:31.137163+00:00, sha a110ed57b24c)
- Data as of 2026-08-30T08:39:29.467469+00:00.