AReaL vs verl-agent
AReaL: AReaL is an open-source, fully asynchronous reinforcement learning training system designed for large reasoning and agentic models. It offers exceptional flexibility, industry-leading speed, and scalability from a single node to over 1,000 GPUs, achieving state-of-the-art performance.; verl-agent: `verl-agent` extends veRL to train LLM agents using reinforcement learning, featuring a novel step-independent multi-turn rollout mechanism. This design ensures high scalability for long-horizon tasks by allowing customizable per-step input structures and memory management.
TL;DR
Training Reasoning Agents: Developing AI agents capable of complex mathematical, coding, and general reasoning tasks.
Training large language model agents for complex multi-turn, long-horizon tasks.
Side-by-Side Comparison
AReaLverl-agentFeatures
AReaLverl-agentUse Cases
AReaLverl-agentBest For
AReaLverl-agentFAQ
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