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alibaba/ROLL

An efficient and user-friendly library for scaling Reinforcement Learning with Large Language Models on large-scale GPU resources.

Core Features

Efficient and user-friendly scaling for RL with LLMs.

Multi-role distributed architecture leveraging Ray for flexible resource allocation.

Integrates cutting-edge technologies like Megatron-Core, SGLang, and vLLM for accelerated training and inference.

Enhances LLM performance in human preference alignment, complex reasoning, and multi-turn agentic interactions.

Supports diverse hardware (GPUs, Ascend NPUs) and LLM architectures (e.g., Qwen3.5).

Detailed Introduction

ROLL is a specialized reinforcement learning library designed to efficiently scale the training and optimization of Large Language Models (LLMs) using extensive GPU resources. It addresses the critical need for robust infrastructure to improve LLM capabilities in areas such as aligning with human preferences, performing complex reasoning tasks, and facilitating sophisticated multi-turn agentic interactions. By employing a multi-role distributed architecture powered by Ray and integrating advanced acceleration technologies like Megatron-Core, SGLang, and vLLM, ROLL provides a powerful and user-friendly solution for developing high-performance LLMs.