slime

中文版

slime is an LLM post-training framework for RL scaling, providing two core capabilities:

1. High-Performance Training: Supports efficient training in various modes by connecting Megatron with SGLang;
2. Flexible Data Generation: Enables arbitrary training data generation workflows through custom data generation interfaces and server-based engines.

slime's design goal is to make these two capabilities reinforce each other without turning the system into a heavy stack of disconnected trainers, rollout services, and agent frameworks. Megatron training, SGLang rollout, custom data generation, reward computation, verifier feedback, and environment interaction all flow through the same training / rollout / Data Buffer path.

This makes slime one of the most battle-tested open RL post-training frameworks: small enough to understand and extend, but validated through complete training loops behind SOTA-level model releases.

Why This Design Matters

• Battle-tested by frontier model training: slime is the RL framework behind GLM-5.3, GLM-5.2, GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5. This validates the full post-training loop, not only isolated examples.
• Correctness-first infrastructure: RL bugs are often silent. slime keeps the dataflow explicit, supports separate rollout-only and train-only debugging paths, and documents reproducibility, fault tolerance, tracing, profiling, and CI as first-class engineering concerns.
• Native by design: slime passes Megatron arguments through directly and exposes installed SGLang arguments with a --sglang- prefix. New upstream training and serving optimizations can be used without adding another abstraction layer inside slime.
• Maximum data-generation freedom: math, code, search, tools, sandboxes, verifiers, environments, multi-agent systems, and long-horizon agentic workflows plug in as data generation or reward workflows. They do not fork the training kernel.
• Lightweight and opinionated: slime focuses deeply on the Megatron + SGLang path used for large-scale RL. By choosing one rollout backend, slime can use SGLang-specific capabilities directly instead of flattening multiple inference engines into a lowest-common-denominator abstraction.

Production Validation

slime has been exercised by the complete workflow needed for release-grade model post-training: large-scale training, high-throughput rollout, weight synchronization, reward/verifier data, checkpointing, debugging, and long-running stability.

Beyond the GLM family, slime also supports:

• Qwen series: Qwen3.6, Qwen3.5, Qwen3Next, Qwen3MoE, Qwen3, Qwen2.5;
• DeepSeek V3 series: DeepSeek V3, V3.1, DeepSeek R1;
• Llama 3.

Native Engine Pass-Through and SGLang Deployment

slime is not just a framework that can call an inference backend. It keeps the Megatron and SGLang control surfaces close to the upstream engines while adding the RL dataflow around them:

• native SGLang argument pass-through: every argument supported by the installed SGLang can be used by adding the --sglang- prefix, such as passing --mem-fraction-static as --sglang-mem-fraction-static;
• native Megatron argument pass-through: slime reads Megatron arguments directly, so Megatron-side parallelism, optimizer, checkpointing, and model options remain available without wrapper code;
• SGLang Config as an optional YAML extension for topology-specific control, such as separate prefill/decode/EPD-style settings, heterogeneous server groups, multi-model serving, and per-group SGLang overrides;
• PD Disaggregation for multi-turn and agentic workloads with different prefill/decode resource needs;
• router policies such as session affinity for multi-turn agents;
• Delta Weight Sync for training/inference disaggregation and large-model update efficiency;
• External Rollout Engines for deployments where serving is managed outside the training job. The SGLang serving side can use an independent environment, and with disk transport can even run on different GPU models or vendors while using full-checkpoint update from disk or delta update over a shared filesystem.

This pass-through design makes slime native from the start. Most upstream engine improvements remain accessible as the engines evolve, while slime focuses on the RL loop, dataflow, synchronization, and correctness checks.

Choosing SGLang as the single rollout backend is also intentional. Multi-backend frameworks often have to abstract over the common subset of several inference engines, which can hide the strongest features of each backend. slime instead optimizes deeply for SGLang so RL workloads can use SGLang-specific serving, routing, caching, disaggregation, and weight-sync behavior directly.

Correctness, Stability, and CI

slime is developed as RL infrastructure, where "the script runs" is not enough. The project maintains CPU unit tests, contract tests for customization hooks, and GPU end-to-end tests covering dense and MoE models, Megatron training paths, SGLang deployment configurations, checkpointing, numerical precision, async rollout, OPD, PPO-style workflows, and debug rollout-then-train replay.

Useful engineering docs:

• CI
• Debugging
• Reproducibility
• Fault Tolerance
• Trace Viewer
• Profiling

Blogs

• Our vision: slime: An SGLang-Native Post-Training Framework for RL Scaling.
• Our ideas on agentic training: Agent-Oriented Design: An Asynchronous and Decoupled Framework for Agentic RL
• v0.1.0 release note: v0.1.0: Redefining High-Performance RL Training Frameworks

Table of Contents

• Why This Design Matters
• Production Validation
• Native Engine Pass-Through and SGLang Deployment
• Correctness, Stability, and CI
• Architecture Overview
• Quick Start
• Ecosystem Built on slime
• Arguments Walkthrough
• Developer Guide
• FAQ & Acknowledgements

Architecture Overview

Module Descriptions:

• training (Megatron): Responsible for the main training process, reads data from the Data Buffer, and synchronizes parameters to the rollout module after training.
• rollout (SGLang + router): Generates new data (including rewards/verifier outputs) and stores it in the Data Buffer. Custom generate functions can wrap this with multi-turn loops, tool calls, environment/sandbox interaction, and verifier-based reward.
• data buffer: A bridge module that manages prompt initialization, custom data, and rollout generation methods (including agentic workflows that produce samples through the same interface).

Quick Start

For a comprehensive quick start guide covering environment setup, data preparation, training startup, and key code analysis, please refer to:

• Quick Start Guide

We also provide examples for some use cases not covered in the quick start guide; please check examples.

Agentic RL examples

For agentic RL workloads, the following examples plug into the standard rollout / Data Buffer loop through customization interfaces — they are not separate frameworks:

• examples/multi_agent: Multi-agent generation via --custom-generate-function-path inside the standard rollout loop.
• examples/search-r1: Search/RAG-style multi-turn generation via --custom-generate-function-path.
• examples/fully_async: Fully-async rollout, useful for long-tail agentic generation where some samples take much longer than others.
• examples/coding_agent_rl: End-to-end SWE coding-agent RL with sandboxed tool use, test-based rewards, and token-correct trajectory segments via --custom-generate-function-path.

See the Customization Guide for which interface to use for a given agentic workflow.

Ecosystem Built on slime

These are not just demos. They are independent systems that use slime as a reusable RL substrate for production-scale post-training, agentic RL, domain RL, and rollout-system research.

🐎 Dressage: Scalable RL for Any Agent and Sandbox

Dressage is an agentic RL training framework built on slime by Alibaba Accio, centered on unified RL for blackbox agents (e.g., OpenCode, OpenClaw) and white loops across any sandbox environment (e.g., bwrap, E2B, Kubernetes). It decouples interaction semantics, execution placement, and token-level trajectory capture through Paddock, Sandbox, and Proxy layers, adapting agent workflows without rewriting their internal loops. Dressage records token-wise logprobs, loss masks, weight versions, and MoE routing, then uses TITO and segment-aware training to turn long-horizon tool interactions into stable RL samples.

⛵ Miles: Enterprise-Grade Reinforcement Learning for Large-Scale Model Training

Miles is an RL post-training framework for large-scale models, built on slime by RadixArk. It stays closely aligned with slime's upstream development while extending it with enterprise-oriented features: deeper SGLang integration, operational tooling, deployment support, and optimizations for new models and hardware. Miles also adds a growing set of production features, including LoRA, TITO, and low-precision training.

🔷 vime: vLLM-Native RL Post-Training Built on slime

vime is a post-training framework built on slime and maintained by the vLLM project. It keeps slime's Megatron training stack, Data Buffer dataflow, and custom data-generation design, with its main change being a rollout backend swapped to vLLM with vllm-router. Starting from an existing slime launch script, adjusting only rollout-related parameters is enough to quickly run training with vime.

🌈 Relax: Asynchronous RL Engine for Omni-Modal Agentic Training

Relax (Reinforcement Engine Leveraging Agentic X-modality) is an omni-modal agentic RL framework open-sourced by the RedAI Infra team, built upon the slime infrastructure stack that combines Ray, Megatron-LM, and SGLang. Relax adopts a service-oriented architecture on Ray Serve with Megatron-LM and SGLang as training/inference backends. It uses TransferQueue to fully decouple Actor, Rollout, ActorFwd, Reference, and Advantage computation onto independent GPU clusters, and introduces DCS (Distributed Checkpoint Service) — an NCCL-broadcast weight-sync engine that streams updated Actor weights to Rollout/ActorFwd/Reference asynchronously and overlaps the transfer with the next training step, enabling fully-async training at configurable staleness. Relax supports end-to-end RL for text, vision, and audio (including Qwen3-Omni) and agentic multi-turn rollouts.

🦞 OpenClaw-RL: Train a Personalized Clawbot Simply by Talking to It

OpenClaw-RL is an RL server for personalized OpenClaw agents. It hosts the OpenClaw model and improves it from prior conversations across deployments, while slime's asynchronous RL infrastructure prevents training from interfering with API serving. It supports two automatic optimization methods: GRPO with binary feedback inferred from subsequent states, and on-policy distillation that extracts hindsight hints from later feedback for the current policy.

⚛️ P1: Mastering Physics Olympiads with Reinforcement Learning

P1 is a family of open-source physics reasoning models trained entirely through reinforcement learning. P1 leverages slime as the RL post-training framework, and introduces a multi-stage RL training algorithm that progressively enhances reasoning ability through adaptive learnability adjustment and stabilization mechanisms. Empowered by this training paradigm, P1 delivers breakthrough performance in open-source physics reasoning.

📈RLVE: Scaling LM RL with Adaptive Verifiable Environments

RLVE introduces an approach using verifiable environments that procedurally generate problems and provide algorithmically verifiable rewards, to scale up RL for language models (LMs). With joint training across 400 verifiable environments, RLVE enables each environment to dynamically adapt its problem difficulty distribution to the policy model's capabilities as training progresses.

⚡ TritonForge: Agentic RL Training Framework for Kernel Generation

TritonForge leverages slime's SFT and RL capabilities to train LLMs that automatically generate optimized GPU kernels. By using a two-stage training approach—supervised fine-tuning followed by reinforcement learning with multi-turn compilation feedback—TritonForge achieves remarkable results in converting PyTorch operations into high-performance Triton kernels.

🚀 APRIL: Accelerating RL Training with Active Partial Rollouts

APRIL introduces a system-level optimization that seamlessly integrates with slime to accelerate the rollout generation phase in RL training. By intelligently over-provisioning requests and actively managing partial completions, APRIL addresses the long-tail generation bottleneck that typically consumes over 90% of RL training time.

🏟️ qqr: Scaling Open-Ended Agents with ArenaRL & MCP

qqr (a.k.a. hilichurl) is a lightweight extension for slime designed to evolve open-ended agents. It implements the ArenaRL algorithm to tackle discriminative collapse through tournament-based relative ranking (e.g., Seeded Single-Elimination, Round-Robin) and seamlessly integrates the Model Context Protocol (MCP). qqr leverages slime's high-throughput training capabilities to enable scalable, distributed evolution of agents in standardized, decoupled tool environments.

☁️ ART: Scalable and Sandboxed Agentic RL on AWS Bedrock AgentCore Runtime

ART (AgentCore RL Toolkit) is an SDK that adapts production agents for RL training on AWS Bedrock AgentCore Runtime. AgentCore Runtime provides auto-scaled and sandboxed agent execution environments well-suited for running many parallel agent rollouts securely. Using ART, user only needs to apply a decorator (@app.rollout_entrypoint) to their agent codes for RL adaption while the same production agent harness is reused directly, where token capture for RL is handled at model gateway layer. ART uses slime as one option of training backends, enabling users to easily optimizing the production agent model with RL training algorithms in slime.

Together, these projects show the main idea behind slime: one high-performance RL kernel can support frontier model post-training, online agent optimization, verifiable environments, omni-modal rollouts, kernel-generation agents, and rollout-system research without changing the core training loop.

Arguments Walkthrough

Arguments in slime are divided into three categories:

1. Megatron arguments: slime reads Megatron arguments directly. You can configure Megatron by passing arguments like --tensor-model-parallel-size 2.
2. SGLang arguments: All arguments for the installed SGLang are supported through pass-through. These arguments must be prefixed with --sglang-. For example, --mem-fraction-static should be passed as --sglang-mem-fraction-static.
3. slime-specific arguments: Please refer to: slime/utils/arguments.py

For complete usage instructions, please refer to the Usage Documentation.

Code Reading Path

Start from the training loop and follow the calls only as deep as needed:

train.py: train
├─ slime/ray/placement_group.py       Ray resource and worker initialization
├─ slime/ray/rollout.py              RolloutManager.generate: rollout orchestration
│  └─ slime/rollout/sglang_rollout.py  Sample generation and reward computation
└─ slime/ray/actor_group.py          RayTrainGroup.async_train: training dispatch
   └─ slime/backends/megatron_utils/actor.py
      ├─ model.py                    Megatron model execution
      └─ loss.py                     RL losses and advantages

On a first pass, treat slime/utils/arguments.py as the configuration entry point. The deployment details in slime/backends/sglang_utils/ and the weight-sync implementations under slime/backends/megatron_utils/update_weight/ can also wait until you need to change those areas.

Developer Guide

• Contributions are welcome! If you have suggestions for new features, performance tuning, or feedback on user experience, feel free to submit an Issue or PR 😊

• Use pre-commit to ensure code style consistency for your commits:

apt install pre-commit -y
pre-commit install

# run pre-commit to ensure code style consistency
pre-commit run --all-files --show-diff-on-failure --color=always

• For debugging tips, please refer to the Debugging Guide

FAQ & Acknowledgements

• For frequently asked questions, please see the Q&A
• Special thanks to the following projects & communities: SGLang, Megatron‑LM, mbridge, OpenRLHF, veRL, Pai-Megatron-Patch and others.
• To quote slime, please use:

@misc{slime_github,
  author       = {Zilin Zhu and Chengxing Xie and Xin Lv and slime Contributors},
  title        = {slime: An LLM post-training framework for RL Scaling},
  year         = {2025},
  howpublished = {\url{https://github.com/THUDM/slime}},
  note         = {GitHub repository. Corresponding author: Xin Lv},
  urldate      = {2025-06-19}
}