THUDM/slime

Health v2 · maintenance only

Adoption not part of the score

8261 stars · 1191 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

slime is an open-source LLM post-training framework for reinforcement learning scaling, connecting Megatron-based training with SGLang-based rollout through a unified training/rollout/data-buffer pipeline. It supports flexible custom data generation workflows including tool use, verifiers, sandboxes, and multi-agent environments, and has been battle-tested in training frontier GLM models.

Use cases

• run RL post-training on large language models
• train LLMs with reinforcement learning at scale
• generate rollout data with custom tools and verifiers
• fine-tune models with agentic workflows and environment feedback
• connect Megatron training with SGLang inference for RL loops
• reproduce frontier-model RL training pipelines

When to choose

• you need a battle-tested RL post-training framework for large LLMs
• your stack already uses Megatron for training and SGLang for serving
• you need flexible custom data generation, reward, and environment workflows
• you want upstream Megatron/SGLang optimizations without extra abstraction layers

When to avoid

• you need a simple supervised fine-tuning pipeline without RL
• you require multiple rollout backends beyond SGLang
• you lack multi-GPU infrastructure for large-scale training
• you want a lightweight single-node fine-tuning tool

Facets

framework · maturity active

llm-training reinforcement-learning machine-learning gpu-computing large-language-models reinforcement-learning machine-learning gpu-computing python rlhf post-training megatron sglang rollout reward-modeling agentic-training gpu linux docker

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markdown · JSON · MCP: product_card(name="THUDM/slime")