Alibaba Open-Sources ROLL Framework: A New Paradigm for Reinforcement Learning Training of Large-Scale Language Models

ROLL is an open-source reinforcement learning framework for large-scale language models developed by Alibaba. Built on the Ray distributed architecture, it integrates cutting-edge technologies like Megatron-Core, SGLang, and vLLM, supporting seamless scaling from single machines to thousand-GPU clusters and providing an efficient solution for post-training of large models.

With the rapid development of large language models (LLMs), the importance of the post-training phase has become increasingly prominent. While traditional supervised fine-tuning (SFT) can enhance the basic capabilities of models, reinforcement learning (RL) has become an indispensable technical path to enable models to truly possess advanced capabilities such as complex reasoning, multi-turn dialogue, and tool calling.

However, applying reinforcement learning to large model training faces many challenges: First, the scale problem—modern large models often have tens of billions of parameters, requiring thousand-GPU clusters for distributed training; second, the efficiency problem—RL training involves multiple links such as model inference, reward calculation, and policy update, and coordinating resource allocation among these links is key; finally, the usability problem—existing RL frameworks often have high thresholds, requiring researchers to write a lot of low-level code to conduct experiments.

Alibaba's recently open-sourced ROLL (Reinforcement Learning Optimization for Large-scale Learning) framework is designed to address these pain points. As a reinforcement learning library specifically built for large-scale language models, ROLL has made innovative breakthroughs in architecture design, training efficiency, and user-friendliness.

The most prominent feature of ROLL is its distributed multi-role architecture built on Ray. Ray is an open-source distributed computing framework, especially suitable for handling heterogeneous computing tasks. ROLL fully leverages this feature of Ray, decomposing the entire training process into multiple independent roles (Actors):

• Learner: Responsible for updating model parameters, usually running on nodes equipped with high-memory GPUs.
• Rollout Worker: Responsible for generating training data, requiring a large amount of inference computing resources.
• Reward Model: Evaluates the quality of generated results and provides feedback signals for policy optimization.
• Reference Model: Provides a benchmark for KL divergence constraints in algorithms like RLHF.

The advantage of this multi-role design lies in flexible resource scheduling. Traditional RL training often uses a synchronous mode where all GPUs wait for the slowest link to complete, causing serious resource waste. ROLL's Rollout scheduler can dynamically allocate tasks—when some rollout workers finish their computations, new generation tasks are immediately assigned to them, thus maximizing GPU utilization.

To improve training efficiency, ROLL has deep integration with current mainstream inference acceleration engines:

Megatron-Core is a large-scale Transformer training library developed by NVIDIA. ROLL implements tensor parallelism and pipeline parallelism through Megatron-Core, supporting simultaneous training of trillion-parameter models on hundreds of GPUs. The latest version has been upgraded to Megatron-Core 0.12 and supports parameter-efficient fine-tuning technologies like LoRA.

vLLM is known for its PagedAttention technology, which can significantly improve the inference throughput of large models. ROLL supports vLLM's dynamic FP8 quantization and remove_padding optimization, further reducing memory usage and increasing inference speed.

SGLang is an emerging structured generation language, especially suitable for RL scenarios requiring strict output formats. ROLL's integration with SGLang makes models more efficient when generating Chain-of-Thought or JSON-structured outputs.

ROLL has built-in mainstream algorithms in the current large model RL field:

PPO (Proximal Policy Optimization) is the most basic policy gradient algorithm, which prevents excessive policy updates by clipping the objective function. ROLL implements the complete PPO process, including advantage estimation, value function learning, and policy update.

GRPO (Group Relative Policy Optimization) is an efficient algorithm used by models like DeepSeek-R1. It does not require separate training of a value model; instead, it calculates advantages through intra-group normalization of multiple answers to the same question. ROLL has optimized the implementation of GRPO, reducing memory overhead.

RLVR (Reinforcement Learning with Verifiable Rewards) is a training paradigm that ROLL focuses on supporting, especially suitable for verifiable tasks such as mathematical reasoning and code generation. Unlike traditional RLHF based on human preferences, RLVR uses verifiable reward signals (such as code execution results and mathematical answer correctness), avoiding the training cost of reward models.

In addition to traditional single-turn text generation tasks, ROLL also supports Agentic RL (Agent Reinforcement Learning), which is a cutting-edge direction in current large model research. In Agentic scenarios, models need to interact with the environment in multiple rounds, call tools, observe feedback, and adjust strategies.

ROLL provides special support for this:

• GEM Environment Definition: A standardized agent environment interface compatible with multiple task types.
• Tool Use Training: Supports specialized training for tool calling capabilities.
• Asynchronous Training Mode: For long-trajectory Agentic tasks, supports asynchronous training to reduce waiting time.
• Stepwise Learning: Supports step-by-step learning algorithms like GiGPO for fine-grained optimization of complex multi-step tasks.

ROLL has been verified on multiple mainstream open-source models, including:

• Qwen Series: From Qwen2.5 (0.5B to 72B parameters) to the latest Qwen3 (8B/14B/32B) and Qwen3-MoE (30A3/235A22), as well as multimodal Qwen2.5-VL and Qwen3-Omni.
• Wan2.2: Supports reward feedback learning for video generation models.
• Custom Models: New model architectures can be adapted via configuration files.

ROLL has made several innovations in training efficiency:

RollPacker technology specifically addresses the long-tail problem in long-trajectory training. In RL training, some samples require very long generation steps, causing other GPUs to idle and wait. RollPacker uses an intelligent packing strategy to group short samples together, maximizing batch utilization.

GPU Partial Overlap technology allows computation and communication to be executed in parallel as much as possible. In distributed training, gradient synchronization often becomes a bottleneck; ROLL hides communication delays through a fine-grained pipeline design.

FSDP2 Strategy provides a more flexible model sharding method, especially suitable for training scenarios of ultra-large models.