ROCK&ROLL: Alibaba's Dual-Framework Collaboration Drives Scalable Agentic RL Applications
# **ROLL + ROCK: End-to-End Agentic AI Training Infrastructure**
**Article #131 of 2025**
*(Estimated Reading Time: 15 minutes)*
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## **01 — Preface**
**ROLL** is an open-source reinforcement learning (RL) framework for large-scale models, developed by **Alibaba’s Future Life Lab** and **Intelligent Engine team**.
It provides a **complete RL training pipeline**, enabling models to **learn task-solving strategies** through interaction with environments.
**Challenge:**
ROLL currently **does not** offer standardized support at the environment service layer. This means users must **build and maintain environments themselves**, increasing the barrier to entry and limiting scalability.
**Solution:**
Alibaba open-sourced **ROCK** — a **powerful environment sandbox** that complements ROLL by providing:
- **Standardized environment interfaces** — unified APIs for easy integration
- **Out-of-the-box sandbox** — secure, preconfigured execution environments
- **High-performance service support** — optimized concurrency and resource scheduling
- **Task diversity support** — covers typical Agentic task scenarios
**Synergy:** Together, ROCK + ROLL deliver **end-to-end solutions** from training framework to environment services — dramatically reducing complexity for Agentic model development.
📦 **Repositories**:
- ROCK → [https://github.com/alibaba/ROCK](https://github.com/alibaba/ROCK)
- ROLL → [https://github.com/alibaba/Roll](https://github.com/alibaba/Roll)
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## **02 — Project Background**
### **2.1 Model Evolution: From Text to Agentic Interaction**
Large Language Models (LLMs) have evolved from **pure text output** to **active environment interaction**.
Modern **Agentic models** — like **GPT‑5**, **Claude 4.x**, and **Gemini‑2.5** — can:
- Engage in **multi-turn dialogues**
- Call **functions and APIs**
- Execute **code**
- Make **real-time decisions** and **act** in environments
**Impact on Businesses:**
Many enterprise workflows **require actions**, not just recommendations:
- **DevOps**: Execute commands, fix system issues
- **Data Analysis**: Generate and run code, create visual reports
- **Customer Service**: Query databases, update records
Agentic capability transforms AI from *"answering"* to *"doing"*.
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### **2.2 Core Requirements for Agentic Model Training**
Four foundational components for high-quality Agentic model training:
1. **Base LLM model** — reasoning, planning, decision-making core
2. **Task & instance descriptions** — problem domain definition + evaluation metrics
3. **RL framework for large models** — algorithms + scalable infrastructure
4. **Environment services** — interactive execution contexts for agents
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**Ecosystem Connection:**
Platforms like **[AiToEarn](https://aitoearn.ai/)** integrate **AI content generation**, **multi-platform publishing** (Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, YouTube, Pinterest, X/Twitter), analytics, and **model ranking**, enabling monetization workflows for AI creators.
Such tools complement ROLL + ROCK by helping creators **deploy and track real-world impact** of Agentic models.
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## **Environment Services — Fuel for RL Engines**
**Analogy:**
If ROLL is the **engine**, **environment services** are the **fuel**.
Without scalable, stable environments:
- RL algorithms **cannot** gather enough interaction data
- Training speed and model quality suffer
**Requirements for high-quality environment services:**
- **High concurrency** → thousands of tasks in parallel
- **Low latency feedback** → rapid training iterations
- **Accurate state management** → reset/rollback safely
- **Flexible scalability** → handle diverse task types
**Dual-Engine Approach:**
1. **ROLL** → customizable RL training
2. **ROCK** → elastic, high-performance environment management
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## **03 — ROLL Framework**
### **3.1 Overview**
**ROLL** is built on **Ray**, designed for **small-scale research → large production**:
- Multi-domain training (math, code, reasoning)
- Native Agentic RL support
- Integration with **Megatron-Core**, **Deepspeed** — supports 5D parallelism
- Sample management, async inference, async training acceleration
- **GEM Interface** — simplifies environment interaction with just:
Initialize environment
observation, info = env.reset()
Interaction loop
while True:
action = llm.generate(observation)
next_observation, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
break
**Benefits:** Quick adaptation to new applications with minimal interface work.
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### **3.2 Environment Service Requirements**
To maximize ROLL's capabilities:
1. **High concurrency** — match ROLL's throughput
2. **Fault tolerance** — redundant deployments protect training stability
3. **Fast state management** — rapid environment launch/reset
4. **Adaptability** — support game, dialog, tool-call, and custom tasks
These are exactly the challenges **ROCK** tackles.
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## **04 — ROCK**
### **4.1 Scaling Capability**
Built on **Ray**, ROCK transforms clusters into **elastic environment pools**:
- **Auto-scale** from 1 → 10,000 environments in minutes
- Support **homogeneous & heterogeneous** environments
- Removes manual cluster setup frustration
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### **4.2 Bash Interaction**
ROCK removes the "black box" problem by enabling **Linux Shell-level control**:
- **Precise Observation** — inspect logs, processes, files in sandbox
- **Proactive Intervention** — modify configs/env variables live
Interaction is delivered via SDK + HTTP API — usable at scale across environments.
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### **4.3 Flexible Deployment**
ROCK supports **write-once-run-anywhere**:
- **Local independent mode** — test tools & sandbox stability
- **Local integrated mode with ROLL** — end-to-end debugging
- **Cloud deployment** — same configs scale instantly to thousands of environments
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### **4.4 Stability**
Enterprise-level guarantees:
- **Isolation** — no cascading failures between sandboxes
- **Predictable performance** — stable resource allocations
- **Fast state ops** — quick start/reset keeps training uninterrupted
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### **4.5 ModelService — Decoupling Agent Logic**
**Core Idea:** Agents keep their logic, ROLL keeps control over training.
Process:
1. Agent sends request from Sandbox → ModelService intercepts
2. ModelService forwards prompt to ROLL
3. ROLL trains model, sends response back to Agent
**Advantages:**
- Complete **decoupling** → no duplicated Agent logic in training framework
- Centralized GPU usage → sandboxes can use CPU only
- Supports **any** custom Agent logic
- Easy maintenance with reduced complexity
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## **05 — Summary & Outlook**
**ROLL + ROCK** = Full-stack Agentic AI training infrastructure.
Benefits:
- 🚀 Scale 1 → 10k environments fast
- 🔄 Smooth dev-to-prod workflow
- 🛡 Enterprise-level stability
- 🧠 Intelligent Agent training with ModelService
Audience:
- Researchers exploring advanced AI
- Developers building enterprise solutions
- Creators integrating AI into interactive apps
**Extra Ecosystem:**
**[AiToEarn](https://aitoearn.ai/)** enables cross-platform AI content monetization, adding real-world deployment and revenue potential to advanced AI models.
GitHub → [https://github.com/yikart/AiToEarn](https://github.com/yikart/AiToEarn)
Docs → [https://docs.aitoearn.ai/](https://docs.aitoearn.ai/)
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### **Take Action**
📦 **Repositories**:
- [https://github.com/alibaba/ROCK](https://github.com/alibaba/ROCK)
- [https://github.com/alibaba/ROLL](https://github.com/alibaba/ROLL)
📚 **Quick Start**:
[Train your first Agent in 5 minutes →](https://alibaba.github.io/ROCK/zh-Hans/docs/rockroll/)
💬 **Community**:
Scan QR below to join and collaborate globally:
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**Final Note:** The future of Agentic AI is collaborative.
Join now — **Let’s ROCK and ROLL!**
