RLinf RL Infrastructure
Scalable reinforcement-learning infrastructure for embodied robotics and agentic AI training.
Per-dimension scores and reasoning
Evidence shows: the repository does not provide permission models, user confirmation mechanisms, data flow transparency, sensitive data handling, dependency security audits, external effect controls, rollback mechanisms, or source attribution. All trust criteria are unmet, hence scores of 0.
Evidence shows: consistent naming and structure in README and config files, but no failure messages or error handling documentation. Dependency availability: pyproject.toml lists dependencies but lacks version pinning or mirror info. Thus, self_consistency scores 2, dependency_availability and failure_messages score 1.
Evidence shows: README lists many scenarios and models but does not clearly define capability boundaries. Trigger precision: config files provide detailed parameters but not trigger conditions. Environment fit: multiple installation options but not all environment requirements. Thus, audience_and_scenarios and capability_boundaries score 2, trigger_precision scores 1, environment_fit scores 2.
Evidence shows: README provides clear information architecture, but install notes are incomplete. Naming stability: version numbers exist but no naming rules. Examples and FAQ: multiple examples but no FAQ. Known limitations not explicitly stated. License is Apache-2.0, complete. Versioning changelog exists. Maintenance responsibility not clear. Thus, information_architecture, examples_and_faq, versioning_changelog score 2, install_notes, naming_stability, known_limitations, maintenance_responsibility score 1, license scores 3.
Evidence shows: output usability: config examples provided but no output format. Marginal value: multiple features but no comparison with other frameworks. Cost-benefit: no performance data. Thus, output_usability scores 1, marginal_value scores 2, cost_benefit scores 1.
Evidence shows: README claims lack specific evidence but cite papers and docs. Cross-source corroboration: external links but no independent verification. Fact-inference separation: not clearly distinguished. Thus, claim_traceability, cross_source_corroboration, fact_inference_separation all score 1.
- The repository does not provide permission models or user confirmation mechanisms, may not be suitable for sensitive data.
- Dependencies are not version-pinned, potential supply chain risk.
- No rollback mechanism provided, updates may not be reversible.
What does this agent do, and when should you use it?
RLinf is an open-source reinforcement-learning infrastructure for embodied AI and agentic AI, spanning supervised fine-tuning, simulated RL, and real-world RL. It supports workflows including PPO, GRPO, SAC, and IQL while using macro-to-micro flow transformation to organize distributed execution. The repository documents FSDP with HuggingFace, SGLang, or vLLM, plus Megatron with SGLang or vLLM for larger-scale workloads. Its embodied examples cover simulators, VLA/VLM/world models, real robots, and data collection; its agentic examples include SearchR1, rStar2, Online Coder, math reasoning RL, and WideSeek-R1. Delivery is documented through a Docker image and a PyPI library, with end-to-end recipes run from provided configurations and scripts rather than through a conversational interface.
RLinf consumes training configurations and scripts, orchestrates policy training, rollouts, and inference backends, and runs workflows such as PPO, Async PPO, GRPO, SAC, IQL, CrossQ, RLPD, RECAP, STEAM, RLT, and OPD. It connects training to FSDP + HuggingFace/SGLang/vLLM or Megatron + SGLang/vLLM, with training designed to scale across GPU nodes. Its embodied workflows cover SFT, simulated RL, real-world RL, and data collection with environments including ManiSkill, LIBERO, RoboTwin, IsaacLab, BEHAVIOR, Genesis, and Polaris, and with models such as π₀/π₀.₅, OpenVLA, GR00T, Qwen-VL, OpenSora, and Wan. The repository also documents CI coverage for unit, agent-reasoning, embodied end-to-end, and scheduler tests, and its workflows produce trained policies for evaluation, deployment, or further fine-tuning; exact deployment commands are not included in the supplied material.
- A robotics research team wants to SFT and then RL-fine-tune OpenVLA or π₀/π₀.₅ policies in ManiSkill or LIBERO using PPO, SAC, or another listed method.
- An embodied-AI team needs a data-collection-to-SFT-to-RL-to-deployment workflow for Franka, XSquare Turtle2, DOS-W1, or GimArm hardware.
- An infrastructure team operating multiple GPU nodes needs to choose between FSDP or Megatron training while using SGLang or vLLM for inference.
- A researcher wants ready-made starting points for IQL, GRPO, Async PPO, DAPO, SAC-Flow, DSRL, RECAP, STEAM, or OPD experiments.
- An agent-training team needs examples for search, coding, or reasoning workflows such as SearchR1, rStar2, AgentLightning Calc-X, Online Coder, math reasoning RL, or WideSeek-R1.
What are this agent's strengths and limitations?
- Covers both embodied robotics and agentic reinforcement learning instead of focusing on one simulator or model family.
- Documents two distinct backend paths: FSDP + HuggingFace/SGLang/vLLM for rapid adaptation, and Megatron + SGLang/vLLM for large-scale training.
- Lists a full embodied workflow from SFT and simulation RL through real-world RL and deployment, across many simulators, model families, and robot setups.
- Supports a broad set of named algorithms, including PPO, GRPO, SAC, IQL, Async PPO, DAPO, RLPD, DSRL, RECAP, STEAM, RLT, and OPD.
- Embodied-RL setup is explicitly described as complex enough to warrant the recommended Docker image, which can raise adoption effort compared with a pure Python training library.
- The supplied material lacks exact installation commands, Docker image tags, hardware specifications, CUDA/Python compatibility ranges, and credential requirements.
- Many documented capabilities depend on particular simulators, robots, cameras, grippers, or external model ecosystems, so unlisted platforms will require integration work.
- The stated throughput and speed figures apply to specific workflows or simulator scenarios; the supplied material does not provide a general benchmark configuration.
How do you install or deploy this agent?
The supplied material confirms that RLinf is available as a PyPI library and recommends its Docker image because embodied-RL environments and dependencies are complex. It does not provide a copyable package-install command, image name or tag, Python/CUDA versions, or credential requirements. A first installation therefore requires following either the documented “Installation Method 1: Docker image” path or the “Install as a Library” path after choosing the intended runtime.
How do you use this agent?
After environment setup, the material identifies a simple embodied-RL example using the ManiSkill3 simulator as the first working path. It also states that users can directly run the provided configurations and scripts for end-to-end SOTA RL reproduction. No exact commands, configuration paths, required environment variables, dataset-download steps, or minimum hardware requirements are included in the supplied evidence, so a reliable copyable invocation cannot be stated here.