Rock & Roll! Alibaba Built a Real-World Training Ground for AI Agents | Open Source

Kreisy Reporting from AF Temple

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Introduction: A Live-Fire Training Ground for Intelligent Agents

Alibaba has officially open-sourced its new project — ROCK — solving one of the biggest challenges in AI training: scaling complex task environments in the real world.

With ROCK, developers can launch standardized one-click deployments for large-scale agent training, removing the need to manually craft and configure environments.

The Perfect Pair: ROLL + ROCK

This forms a seamless synergy with Alibaba’s earlier RL training framework ROLL:

• ROLL → Teaches AI how to think (training algorithms)
• ROCK → Gives AI a practice arena (environment sandbox)

Together, they create a complete agent training loop — enabling research teams to move from small, single-machine experiments to massive, multi-node cluster training.

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Environment Services — The Overlooked Link in Agent Evolution

State-of-the-art LLMs are shifting toward Agentic AI — models that interact deeply with the outside world.

Why Models Need More Than Just Talk

Modern agents not only converse, but also:

• Call tools
• Execute code
• Invoke APIs
• Perform real-world actions

From a commercial standpoint, this means automation pipelines can execute immediate, real actions, rather than just producing recommendations.

The Four Puzzle Pieces of Agent Training

Training capable agents requires more than a strong LLM “brain” — they must learn sequencing, planning, and execution in realistic environments. This involves:

• The Brain — LLM
• The Test Paper — Task descriptions
• The Coach — RL framework
• The Training Ground — Environment service (often forgotten)

Why Environment Stability Matters

For Agentic models:

• Stability and efficiency of the environment service directly control scalability.
• Training requires:
• Massive concurrency — thousands to tens of thousands of parallel runs
• Low-latency feedback — millisecond responses
• Robust state control — reset, rollback, recovery
• Flexibility — handle varied task complexities

Without this infrastructure, environment bottlenecks become the choke point of the pipeline.

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ROLL — High-Powered Agentic RL Engine

ROLL is built on Ray for large-scale LLM reinforcement learning — scaling from research prototypes to 100B-parameter, multi-thousand-GPU deployments.

Key ROLL Features

• Multi-domain training: math, code, reasoning
• Native Agentic RL support: games, multi-turn dialogue, tool calls, code agents
• Deep integration: Megatron-Core, Deepspeed, 5D parallel execution
• Sample-level generation, asynchronous inference/training

ROLL uses asynchronous environment interaction and redundancy sampling, with a clean standard interface — GEM:

env.reset()
env.step(action)

Environment developers simply implement `reset` and `step`, instantly connecting tools from simple games to complex APIs.

The Scaling Challenge

ROLL demands environment scalability — and ROCK delivers exactly that.

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ROCK — Reinforcement Open Construction Kit

Mission: Scalable Agent Training

ROCK breaks the traditional ceiling on training environments:

• Built on Ray — abstracts heterogeneous clusters into a unified resource pool.
• Elastic deployment — spin up 10K+ environments in minutes.
• Mixed-mode clusters — homogeneous and heterogeneous environments run side-by-side.

Transparent Debugging with “God Mode”

• Programmable Bash interaction via SDK/HTTP API.
• Check logs, files, processes across thousands of Sandboxes as if local.

Deployment Modes

• Local standalone — unit testing and basic sanity checks.
• Local integrated debugging — run full end-to-end training with ROLL.
• Cloud-scale deployment — move to production with zero config changes.

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Stability Standards

ROCK meets Alibaba internal infra standards:

• Fault isolation — one crash never cascades.
• Precision scheduling — no noisy neighbors, consistent performance.
• Fast recovery — restart and reset in seconds.

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ModelService — Decoupling Agent Logic from Training

Previously, Agent business logic had to be embedded inside the training framework — creating code conflicts and heavy maintenance burdens.

ROCK’s ModelService is an intelligent model proxy inside each Sandbox, enabling clean separation:

• Question — Agent sends prompt.
• Intercept — ModelService passes prompt back to ROLL.
• Answer — ROLL calculates rewards/optimizations, returns result.

Benefits:

• Complete decoupling — independent codebases.
• Training control stays with ROLL.
• Cost efficiency — CPU Sandboxes + centralized GPU inference.
• Broad compatibility — supports custom agent logic.

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The ROCK + ROLL Advantage

With ROCK open-sourced:

• Elastic scalability — 1 → 10K environments in minutes
• Production-ready stability — fault isolation & rapid recovery
• Seamless workflow — dev → test → cloud deployment smoothly
• Architectural innovation — ModelService enables modular design

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Related Ecosystem — AiToEarn

Platforms like AiToEarn官网 complement this infrastructure by enabling multi-platform AI content monetization — from Douyin, Kwai, and WeChat to Instagram, YouTube, and X.

With:

• Integrated AI generators
• Cross-platform publishing
• Analytics and AI模型排名

AiToEarn pairs perfectly with ROCK/ROLL workflows, applying scalable agent logic to content automation.

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Getting Started

Repositories:

• ROCK: https://github.com/alibaba/ROCK
• ROLL: https://github.com/alibaba/ROLL

Quick Start Guide: Train your first agent in 5 minutes

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

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Bottom Line:

ROCK and ROLL transform agent training from elite tech experiments into a standard, industrialized process accessible to every developer.

Whether for research, enterprise automation, or creative AI content generation — this infrastructure is designed to scale with you.