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verl-agent
Tool for training LLM agents using reinforcement learning.
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What is verl-agent?
verl-agent is tool for training LLM agents using reinforcement learning.
About
verl-agent is an extension of veRL designed for training large language model (LLM) agents through reinforcement learning (RL). It features a step-independent multi-turn rollout mechanism, allowing for customizable input structures and memory management, making it scalable for long-horizon tasks. With a diverse set of RL algorithms and environments, it supports the development of reasoning agents in both visual and text-based tasks.
Strengths
- Highly customizable input and memory management.
- Scalable for long-horizon RL tasks.
- Supports a variety of RL algorithms.
- Includes rich environments for diverse training scenarios.
- Active community with ongoing updates and improvements.
Limitations
- Complex setup may require familiarity with RL concepts.
- Limited documentation on advanced features.
- Performance may vary based on the chosen environment and algorithm.
- Requires significant computational resources for large-scale training.
- Still in development, which may lead to instability in some features.
Use Cases
Training LLM agents for complex tasks in ALFWorld.Developing reasoning agents for visual and text-based environments.Implementing customizable memory modules for RL training.Conducting experiments with various RL algorithms like GiGPO and PPO.Creating multi-modal agents that can process both text and images.
Integrations
veRLHugging Face ModelsQwen3OpenManus-RLROLL