ROLL

An efficient, user-friendly reinforcement learning library for training large language models at scale, covering preference alignment, complex reasoning and multi-turn agentic interaction.

Models

Visit website alibaba.github.io

ROLL is a reinforcement learning library for large language models designed to make efficient use of large-scale GPU resources. It targets human preference alignment, complex reasoning and multi-turn agentic interaction as its main use cases.

It uses a multi-role distributed architecture built on Ray for flexible resource allocation and heterogeneous task scheduling, and integrates technologies such as Megatron-Core, SGLang and vLLM to accelerate both training and inference. Recent releases add support for Qwen3.5 dense and mixture-of-experts models, on-policy distillation, an FSDP2 training strategy, Megatron combined with LoRA, partial GPU overlapping, and support for Ascend NPUs.

It is written in Python and released under the Apache 2.0 license.

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