RLinf

RLinf: Reinforcement learning infrastructure for training embodied and agentic AI systems.

RLinf is a flexible open-source reinforcement learning infrastructure designed for embodied and agentic AI. It acts as a backbone for training models, supporting open-ended learning and continuous generalization across diverse hardware and simulators. The platform integrates with various GPUs like Biren, Moore Threads, Huawei Ascend, and AMD, alongside support for PyTorch. It features a unified robotics interface for managing real-world hardware, tasks, and rollout wrappers. Researchers and developers use it to run reinforcement learning, supervised fine-tuning, and evaluation on models like Pi0, LIBERO, and Cosmos3.

RLinf at a glance

Best for: Software engineer, Researcher
Runs on: Linux, API, open source
Pricing: Free
Released: August 2025
Category: AI coding tool
Website: github.com/RLinf/RLinf

Key features

Manage real-world robots with a unified interface
Support reinforcement learning on multiple GPU architectures
Run supervised fine-tuning and evaluation pipelines
Execute distributed data shuffling via FUSCO
Integrate with simulators like LIBERO and BEHAVIOR
Support online and offline policy optimization

Use it for

Train reinforcement learning models for robotic manipulation
Fine-tune vision-language-action models
Evaluate policies within robotic simulators
Execute real-world online policy learning

Alternatives to RLinf

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OpenRLHF — High-performance distributed architecture and unified agent-based execution
ART — Enhances LLM agent performance and reliability using reinforcement learning techniques.
NeMo Molt — It scales fully-async agentic RL to frontier MoE scale with a small PyTorch stack.
Agent Lightning — Train existing artificial intelligence agents with reinforcement learning and zero code changes.
Rax — Exploration compiles into code that runs up to 241 times faster.
Stable Baselines3 — Provides reliable, standard PyTorch implementations for reinforcement learning.
Ray — It orchestrates distributed infrastructure for any AI workload at any scale.
Karotte — It runs tasks in isolated virtual machines for safety and consistency.
AReaL — Bridges foundation model training with modern agent-based applications.
Ludwig — Train and fine-tune complex AI models using a single YAML configuration file.
LoongForge-VLDA — Achieves high success rates on simulation benchmarks and real robots.

All RLinf alternatives