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How to use SGLang as the rollout engine for RL post-training with SGLang

Plug SGLang into an RL framework such as verl, slime or AReaL to generate rollouts fast.

Short answer: Pick an RL framework that integrates SGLang: Miles, slime, AReaL, Tunix or verl; then install SGLang alongside it; configure the framework to use SGLang for rollouts. All steps.

Who it is forResearchers running reinforcement learning on open models.

The problemRollout generation is often the bottleneck in RL training loops.

The outcomeAn RL training setup that uses SGLang for rollout generation.

What you need first

  • A supported GPU or accelerator (NVIDIA, AMD, TPU and others)
  • Docker, or a Python environment with uv

Steps

  1. Pick an RL framework that integrates SGLang: Miles, slime, AReaL, Tunix or verl
  2. Install SGLang alongside it
  3. Configure the framework to use SGLang for rollouts
  4. Measure rollout throughput against your previous setup

Prompt for your agent

Configure <verl or slime> to use sgl-project/sglang for rollout generation on <model>, run a short training job, and compare rollout throughput with the default engine.

Caveat.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for sgl-project/sglang and "Use SGLang as the rollout engine for RL post-training"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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