ROLL is an open-source reinforcement learning library from Alibaba, available on GitHub with about 3,433 stars under the Apache-2.0 license and actively developed in 2026. Its full name, Reinforcement Learning Optimization for Large-Scale Learning, describes its goal: make RL training for large language models efficient, scalable, and genuinely user-friendly for teams with serious GPU resources.
ROLL targets the workloads that define modern post-training: human preference alignment with RLHF, verifiable-reward training with RLVR for math and reasoning, and multi-turn agentic interaction. Its multi-role distributed architecture, built on Ray, allows flexible resource allocation and heterogeneous task scheduling across a cluster. Under the hood it integrates proven engines such as Megatron-Core for training and SGLang and vLLM for fast rollout generation, so sampling does not become the bottleneck. Ready-made examples cover recent model families, including dense and mixture-of-experts variants, and the project publishes research results such as its RollArt system work. Configuration is file-driven, which keeps large experiments reproducible. Extensive documentation and example configurations for popular open model families lower the barrier for teams moving from supervised fine-tuning to reinforcement learning, and the project is developed openly by Alibaba researchers together with external contributors from the wider reinforcement learning community.
ROLL is free and open source under the Apache-2.0 license. The real cost is the GPU cluster you run it on.
ROLL is aimed at AI labs and platform teams doing LLM post-training, researchers studying reinforcement learning for reasoning, and engineers who need RL pipelines that scale beyond a single node.