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-5.2, 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](docs/en/advanced/sglan