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  • Get Started
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        • Real-World RL
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        • Orbbec Cameras
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      • Dual-Arm Franka
        • Collection / SFT / Deployment
        • VR HG-DAgger
        • Collection / SFT / Deployment (OpenPI PyTorch)
      • ARX X5
      • YAM
        • PICO Data Collection
        • Pi0.5 Training and Evaluation
      • GimArm
      • XSquare Turtle2
      • DOS-W1
      • Piper
      • SO101
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    • Embodied Models
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      • MLP
      • π₀ / π₀.â‚…
        • Short Memory
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      • GR00T
      • Dexbotic
      • StarVLA
      • MolmoAct2
      • LingbotVLA
        • LingbotVLA 1.0
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      • Evo-1
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      • Psi0
    • World Models
      • OpenSora
      • Wan
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      • Reward Model Workflow
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      • FastWAM
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      • SAC-Flow
      • DSRL
      • RECAP
      • STEAM
      • Co-Training
      • IQL (D4RL)
      • OPD
      • RL Token
    • Image and Video Generation
      • Diffusion-NFT
      • DiffusionART
    • Agents
      • WideSeek-R1
        • Environment and Tool Setup
        • Training
        • Evaluation
      • AgentLightning
      • Coding Online RL
      • Search-R1
        • Qwen2.5-3B Search-R1
        • GLM-4.7-Flash Search-R1
        • Asynchronous Search-R1
      • rStar2
      • Math Reasoning
        • Qwen2.5 GRPO
        • Qwen2.5 PPO
        • Moonlight-16B GRPO
      • Qwen3 VL GRPO
    • Systems
      • FUSCO High-Performance MoE Communication Library
  • Evaluation
    • Get Started
      • Overview
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    • Benchmark Guides
      • Real-World Evaluation
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        • DreamZero SGLang Evaluation
        • Cosmos3 SGLang Evaluation
      • ManiSkill OOD Evaluation
      • PolaRiS Evaluation
      • RoboTwin Evaluation
      • SIMPLE Evaluation
    • Reference
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  • Guides
    • Configure
      • Basic Configuration
      • Embodied Configuration
      • Agentic Configuration
      • Logging
    • Launch & Scale
      • Multi-Node Training
      • Heterogeneous Clusters
      • Cloud-Edge Collaboration
      • Real-World Robots
      • Teleoperation
    • Data & Checkpoints
      • Data Collection
      • Checkpoint Conversion
      • Resume Training
    • Performance
      • RTC
      • Env Decoupled Mode
      • Observation Compression
      • LoRA
      • Auto Placement
      • Dynamic Scheduling
      • Profiling
      • 5D Parallelism
      • Asynchronous Agentic RL
    • Agent Workflows
      • Agentic Guides
    • Rollout Engines
      • ApxInf
      • SGLang Server & Router
      • InferenceHTTPClient
      • SGLang Version Switching
      • PhyAI
  • Concepts
    • Execution
      • RLinf Execution Flow
      • M2Flow Programming Flow
      • Worker and WorkerGroup
      • Cluster
      • Channel
      • Trajectory Collector
      • Collective Communication
    • Scheduling
      • Placement
      • Execution Modes
      • Replay Buffer
    • Robotics Interface
    • Robotics Architecture
    • Real-World Tasks and Environments
  • Reference
    • API
      • Worker Interface
      • Placement Interface
      • Cluster Interface
      • Channel API
      • Actor Interface
      • Rollout Interface
      • Environment Interface
      • Data Interface
      • Embodied Data Interface
      • Replay Buffer
    • Algorithms
      • PPO
      • GRPO
      • DAPO
      • Reinforce++
      • SAC
      • CrossQ
      • RLPD
      • IQL
      • Async PPO
    • Configuration
      • Training Configuration
      • Training Metrics
  • Extending
    • Extending Overview
    • New Environment
    • New Real-World Tasks
    • New Robot
    • New Model with FSDP
    • New Model with Megatron
    • Adapting an Embodied Model in SGLang Server
    • New SFT Model
    • Advanced Integrations
      • Megatron-Bridge
      • Weight Synchronization
  • Resources
    • Why RLinf
    • Blog
      • Comparison with VeRL
      • A First Look at the “Last Mile” of Agent Deployment: Cursor Online Reinforcement Learning
      • Accelerating the “ImageNet Moment” of Embodied AI: RLinf Brings a 25Ă— System Optimization to BEHAVIOR
      • RLinf x SGLang: Integrating Cosmos3 from Fine-Tuning to Efficient Parallel Evaluation
      • RLinf Ă— WorldLoop: Extending WoVR with a Training-Free VLM Reward
      • RLinf Ă— MUSA: Reproducing WoVR on Moore Threads S5000
    • Publications
      • STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
      • USER: A Unified and Extensible System for Online Real-World Policy Learning in Embodied AI
      • RLinf-VLA: A Unified and Efficient Framework for VLA+RL Training
      • Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models
      • RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation
      • DynaRL: Flexible and Dynamic Scheduling of Large-Scale Reinforcement Learning Training
      • Ď€RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
      • WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
      • WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning
    • v0.1 & v0.2 Release Notes
    • v0.3 Release Notes
    • v0.4 Release Notes
    • FAQ
    • Contributor Policy
  • Guides
  • Launch & Scale

Launch & Scale#

Use these guides when you need to start runs beyond a single default machine or connect RLinf to physical hardware.

Guide

What you get

Multi-Node Training

Launch a run across multiple nodes.

Heterogeneous Clusters

Configure mixed hardware and node groups.

Cloud-Edge Collaboration

Split inference and training across cloud and edge.

Real-World Robots

Run RL on physical robot hardware.

Teleoperation

Let an operator take over from the policy mid-rollout.

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Training Visualisation

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Multi-Node Ray Cluster Setup

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