TIGER-AI-Lab/verl-tool

Health v2 · maintenance only

Adoption not part of the score

1036 stars · 89 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

VerlTool is a unified, extensible framework built on verl for training LLM agents with tool use via reinforcement learning. It decouples actor rollout generation from environment interaction and ships training recipes like Search-R1, ToRL, and DAPO.

Use cases

• train an LLM agent to call tools with reinforcement learning
• reproduce Search-R1 style search-augmented RL training
• run asynchronous trajectory-level RL rollouts with tool calling
• train models for NL2SQL with tool feedback
• add custom tool environments to an RL training pipeline
• benchmark tool-using LLM agents

When to choose

• you want to RL-train LLMs that interact with external tools or environments
• you already use verl and need tool-call rollout support
• you need reproducible recipes like Search-R1, ToRL, or DAPO

When to avoid

• you only need inference-time agent orchestration without RL training
• you need a simple SFT-only fine-tuning pipeline
• you lack GPU resources for large-scale RL training

Facets

framework · maturity active

reinforcement-learning llm-training agent-framework machine-learning reinforcement-learning large-language-models machine-learning python tool-use rl-training verl llm-agents rollout tool-agent research ai-agents gpu linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="TIGER-AI-Lab/verl-tool")