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How to run multi-turn RL against vLLM or SGLang rollouts with Halo

Train a policy with asynchronous GRPO where Ray actors roll out environments on vLLM or SGLang.

Who it is forA team building an RL post-training loop for a model that needs multi-turn interaction with verifiable environments.

The problemKeeping rollout generation and gradient updates in sync across inference servers and a trainer is hard to build from scratch.

The outcomeAn async GRPO run that pushes updated weights to the rollout servers and logs training progress.

What you need first

  • Docker installed on a host with an NVIDIA GPU (Hopper H100/H200 or Blackwell B200/B300 images are published)
  • Network access to public.ecr.aws to pull the Halo image anonymously
  • A Hugging Face model or the example configs in the examples/ folder
  • Enough GPU memory for the chosen model and method (the README shows single-GPU LoRA and 8-GPU runs)

Steps

  1. Pull the training image and the matching inference image, for example docker pull public.ecr.aws/whitecircle/halo:hopper and the vLLM image public.ecr.aws/whitecircle/halo:vllm-0.26.0.
  2. Read the design of the async loop in human-docs/training-methods/async-grpo-environments.md.
  3. List the available methods and tools: halo launch --list and halo run --list.
  4. Launch the environment GRPO method named in the list with its YAML config: halo launch environmental-grpo <config.yaml> --dry-run, then run it without --dry-run. (inferred, check the README)

Prompt for your agent

In the whitecircle/halo repo, read human-docs/training-methods/async-grpo-environments.md and then launch the environmental-grpo method with <target-config> inside the Halo image. Expected output: a running async GRPO job that reports rollouts and weight syncs. Verify with `halo launch --list` that environmental-grpo is available before launching.

Caveat. The README names the environmental-grpo method but does not show its config file, so you must write or pick a YAML config before the launch command will work.

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