Alibaba's newest open-source endeavor, ROCK, tackles the most formidable obstacle in intelligent agent training: the scarcity of scalable and standardized environments for real-world interactions.

Previously, developers frequently faced the daunting task of manually configuring environments for intricate tasks. This involved setting up dependencies, debugging states, resolving compatibility issues, and more. Such a series of engineering tasks made it challenging to scale up training efforts. ROCK aims to standardize the 'training arena,' offering features like one-click deployment, automatic scaling, monitoring, and debugging.

This innovation means developers can, for the first time, train agents without the need for 'handcrafted environments,' enabling environments to scale alongside models.

Simultaneously, ROCK synergizes powerfully with Alibaba's previously open-sourced reinforcement learning training framework, ROLL:

Together, they form a closed loop, combining an algorithm engine with environmental fuel for intelligent agent training. This addresses the core challenges that have long impeded the intelligent agent training process.

Large language models are transitioning from mere 'language tools' to 'intelligent agents capable of interacting with the external world.' They can now execute code, call APIs, fill out forms, browse the web, and operate software.

To truly excel, these models must undergo thousands, or even tens of thousands, of interactive training sessions in real-world settings.

However, systems capable of supporting such a massive number of concurrent environment instances are extremely complex and often become the biggest obstacle in the training journey.

A high-quality environment system must:

Accommodate thousands to tens of thousands of independent instances running simultaneously.

Provide feedback at the millisecond level.

Be capable of reproducing, tracing back, and resetting any state.

Seamlessly integrate with various task types (web, code, toolchains, multi-round interactions).

Maintain stability over extended periods of operation.

These requirements pose an overwhelming challenge for most teams. Alibaba introduced ROCK to tackle this widespread industry issue.

Built on Ray, ROLL is tailored for large-scale reinforcement learning (RL) training of large models. It offers a comprehensive suite of capabilities, ranging from small-scale experiments to large-scale production training:

Supports multi-task mixed training in areas like mathematics, reasoning, and code.

Facilitates multi-step decision-making training in multi-round dialogues, tool calls, and code execution.

Deeply integrates with frameworks such as Megatron-Core and DeepSpeed, supporting multi-dimensional parallelism.

Provides asynchronous inference, asynchronous training, and efficient sample management mechanisms.

Employs a minimalist universal interface, GEM (reset/step), for lighter environment adaptation.

ROLL is essentially a high-performance training engine. However, to operate effectively, it requires a sufficient, stable, and scalable environment as fuel—this is where ROCK comes in.

ROCK (Reinforcement Open Construction Kit) has a clear mission: to overcome environmental scalability bottlenecks through engineering.

Leveraging Ray, ROCK abstracts underlying resources into a unified environmental resource pool. This includes:

Modifying configurations to launch thousands of parallel environments in mere minutes.

Supporting both homogeneous and heterogeneous environments running concurrently in a single cluster.

This significantly lowers the barrier for agent training, transitioning from single-machine experiments to cluster-scale deployment.

Traditional distributed environments are notoriously difficult to debug. ROCK, however, supports remote interaction via SDK or HTTP API, enabling inspection of environment file systems, logs, and process states. Additionally, it allows for real-time modification of environment variables and control of environment behavior.

ROCK also offers three usage modes:

Local standalone environment: Quickly validate dependencies and test environment behavior.

Local integrated debugging: Interface with ROLL to test the complete pipeline.

Cloud-scale deployment: Automatically scale to thousands of instances without code modifications.

Rock & Roll, working in tandem, form a training closed loop for the intelligent agent era. This makes the process replicable, scalable, and industrializable. Roll provides a large-scale RL training engine for models to learn correct decision-making, while ROCK offers a scalable, multi-environment training ground for more stable interaction data.

Whether for researchers, system architects, or independent developers, this toolchain is essential infrastructure for entering the Agentic AI era.

References:

https://mp.weixin.qq.com/s/yX-0TBFWPCIJES17aJnXrA

https://alibaba.github.io/ROCK/docs/Getting%20Started/rockroll/

https://github.com/alibaba/ROCK