langfengQ/verl-agent

Health v2 · maintenance only

Adoption not part of the score

2256 stars · 216 forks observed · 2026-08-28

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

verl-agent is an extension of the veRL framework for training LLM and VLM agents via reinforcement learning, featuring step-independent multi-turn rollouts and customizable history/memory management. It is also the official implementation of the GiGPO (Group-in-Group Policy Optimization) algorithm for fine-grained credit assignment in long-horizon agent training.

Use cases

• train llm agents with reinforcement learning
• implement gigpo for long-horizon agent training
• run multi-turn RL rollouts on ALFWorld or WebShop
• fine-tune Qwen models as interactive agents
• compare group-based RL algorithms like GRPO and GiGPO
• train vision-language agents in visual environments

When to choose

• you need RL post-training of LLM agents over multi-turn, long-horizon tasks
• you want critic-free group-based RL with fine-grained step-level credit assignment
• you need customizable per-step input structures and memory modules during rollouts
• you want to reproduce or extend GiGPO, HGPO, or GraphGPO research

When to avoid

• you only need single-turn RLHF or SFT without agent-environment interaction
• you need a production inference or serving framework rather than a training framework
• you lack GPU resources for large-scale RL rollouts

Facets

framework · maturity active

llm-training reinforcement-learning agent-framework machine-learning reinforcement-learning large-language-models machine-learning deep-learning python rl-training grpo gigpo multi-turn-agents verl llm-agents research-code ai-agents gpu linux

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For agents

markdown · JSON · MCP: product_card(name="langfengQ/verl-agent")