About

slime is an open-source LLM post-training reinforcement learning framework developed by the THUDM team at Zhipu AI. It serves as the RL training infrastructure behind the GLM family of models, from GLM-4.5 through GLM-5.2. At its core, slime seamlessly connects the Megatron training engine with the SGLang inference framework, so training, inference, data generation, reward computation, and environment interaction all share a single unified pipeline. The key philosophy behind slime is to avoid turning the system into a heavy stack of separate trainers, inference services, and agent frameworks — instead, every component collaborates through a unified training/inference/data buffer path, keeping the system simple and scalable.

Key Features

Validated on frontier models: slime is the RL training framework behind SOTA-level models including GLM-5.2, GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5, fully verified across complete training cycles.

Native Megatron + SGLang integration: Megatron parameters are passed through directly, while SGLang parameters are exposed with a --sglang- prefix, so upstream training and inference optimizations require no extra wrapper layers.

BF16 training + FP8 inference: large MoE models use Megatron BF16 training combined with SGLang FP8 inference, with support for FP8 KV Cache to increase effective cache capacity.

Agent workflows as data generation: tool calling, sandbox interaction, verifier rewards, and multi-agent loops can all be plugged into the same training pipeline.

Flexible Agentic RL: supports agentic reinforcement learning training, code generation with verifier rewards, and long-chain reasoning scenarios.

Pricing & Fees

Single unified pipeline that connects training, inference, data generation, reward computation, and environment interaction.
Designed to avoid the complexity of stacking separate trainers, inference services, and agent frameworks.
Direct parameter passthrough between Megatron and SGLang eliminates the need for additional abstraction layers.
Combines BF16 training with FP8 inference and FP8 KV Cache to reduce memory overhead while maintaining model quality.

Who It's For

slime is built for LLM post-training — applying RL alignment training to pretrained models. It is also well suited for Agentic RL research, where developers explore reinforcement learning for tool calling and multi-step reasoning. Additionally, it supports model fine-tuning and compression workflows by leveraging the combination of FP8 inference and BF16 training to reduce resource requirements.

About

Key Features

Pricing & Fees

Who It's For

Reviews (0)

Would you recommend slime - GLM RL Training Framework? Leave a comment

Related Tutorials

Related Tools