Example Gallery

This section presents the collection of examples currently supported by RLinf, showcasing how the framework can be applied across different scenarios and demonstrating its efficiency in practice. This example gallery is continuously expanding, covering new scenarios and tasks to highlight RLinf’s flexibility and efficiency.

Embodied intelligence is RLinf’s primary focus. The embodied gallery is split into five entry points — pick the one that matches your starting question:

Simulators

Start from a benchmark — LIBERO, ManiSkill, RoboTwin, IsaacLab, and more.

RL with Embodied Simulators

Robots

Run on physical robot hardware — the Franka family plus GimArm, XSquare Turtle2, and DOS-W1.

RL with Real-World Robots

Models

RL-fine-tune a specific model family — π₀, GR00T, Lingbot-VLA, OpenSora, Wan, and more.

RL on Embodied Models

SFT

Supervised fine-tuning recipes that produce strong RL cold-start checkpoints.

SFT for VLA / WAM Models

Algorithms

Algorithm-centric examples — DAgger, RECAP, DSRL, IQL offline RL, sim-real co-training, MLP / SAC-Flow.

Algorithms for Embodiment

Beyond embodiment:

Agents

Math reasoning and agentic AI workflows, in both single-agent and multi-agent settings.

Agentic Scenarios

Systems

Flexible, dynamic scheduling of compute resources across the most suitable hardware devices.

System-level Optimizations