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docs_roll/docs/DesignImplementation/AgenticPipeline.md

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## Agentic Pipeline Architecture Diagram
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![Agentic Pipeline Architecture Diagram](../../static/img/AgenticPipeline.jpg)
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![Agentic Pipeline Architecture Diagram](https://img.alicdn.com/imgextra/i1/O1CN01WtFEA01siugMLjRsj_!!6000000005801-2-tps-4602-4230.png)
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## AgenticPipeline
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docs_roll/docs/DevelopmentGuide/support_new_models_en.md renamed to docs_roll/docs/DevelopmentGuide/support_new_models.md

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docs_roll/docs/QuickStart/config_system.md

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- In WorkerConfig, ModelArguments, TrainingArguments, GeneratingArguments, DataArguments, and backend StrategyArguments held by the role are defined as needed
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- WorkerConfig can be extended as needed to construct new application configurations
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![RLVRConfig Diagram](../../static/img/roll_config_diagram.png)
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![RLVRConfig Diagram](https://img.alicdn.com/imgextra/i2/O1CN012otRDy1m5AcR0Dcsx_!!6000000004902-2-tps-3034-1742.png)
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## Mapping between YAML Configuration and PipelineConfig
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docs_roll/docs/QuickStart/multi_nodes_quick_start.md

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bash examples/agentic_demo/run_agentic_pipeline_frozen_lake_multi_nodes_demo.sh
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```
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After the Ray cluster starts, you will see log examples like the following:
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![log_ray_multi_nodes](../../static/img/log_ray_multi_nodes.png)
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![log_ray_multi_nodes](https://img.alicdn.com/imgextra/i3/O1CN01sq2KKz1vBQZViSB0E_!!6000000006134-2-tps-2758-204.png)
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3. Configure environment variables on the worker node
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1. Configure environment variables on the worker node
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```shell
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export MASTER_ADDR="ip of master node"
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export MASTER_PORT="port of master node" # Default: 6379
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export GLOO_SOCKET_IFNAME=eth0
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```
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4. Connect to the Ray cluster started by the master node on the worker node:
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1. Connect to the Ray cluster started by the master node on the worker node:
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```shell
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ray start --address='ip of master node:port of master node' --num-gpus=2
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```

docs_roll/docs/QuickStart/single_node_quick_start.md

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```
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Example log screenshots during pipeline execution:
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![log_pipeline_start](../../static/img/log_pipeline_start.png)
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![log_pipeline_start](https://img.alicdn.com/imgextra/i2/O1CN015wRcUb1EkfZQEjpEI_!!6000000000390-2-tps-2868-650.png)
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![log_pipeline_in_training](../../static/img/log_pipeline_in_training.png)
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![log_pipeline_in_training](https://img.alicdn.com/imgextra/i2/O1CN01Iaiv7H1HYi4hu8Fat_!!6000000000770-2-tps-2876-904.png)
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![log_pipeline_complete](../../static/img/log_pipeline_complete.png)
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![log_pipeline_complete](https://img.alicdn.com/imgextra/i1/O1CN016YSeUs1F5jmtyFA5V_!!6000000000436-2-tps-1904-206.png)
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## Reference: Single V100 GPU Memory Configuration Key Points
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```yaml

docs_roll/docs/UserGuide/agentic/Tool_Use.md

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}
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```
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![math dapo + python_tool](../../../static/img/math_python_tool.png)
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![math dapo + python_tool](https://img.alicdn.com/imgextra/i1/O1CN01NNucNY1WdwqE81Une_!!6000000002812-2-tps-2268-726.png)
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### QA + SearchTool
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#### Training Configuration
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- HotpotQA: `examples/qwen3_agentic_gem/gem_math_hotpotqa.yaml`
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- HotpotQA + Search: `examples/qwen3_agentic_gem/gem_math_hotpotqa_search.yaml`
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![qa + search](../../../static/img/qa_search.png)
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![qa + search](https://img.alicdn.com/imgextra/i2/O1CN01rpk4P91iCGHwx2qv6_!!6000000004376-2-tps-2262-714.png)
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#### Search Engine Startup Method
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docs_roll/i18n/en/code.json

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},
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"Commits": {
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"message": "Commits"
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},
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"Follow us on X": {
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"message": "Follow us on X"
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}
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}

docs_roll/i18n/zh-Hans/code.json

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"message": "分布式框架"
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},
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"Ray-based distributed architecture supporting mainstream engines like vLLM, SGLang, Megatron-Core, seamlessly scaling from single machine to large GPU clusters": {
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"message": "基于 Ray 的分布式架构,支持 vLLM、SGLang、Megatron-Core 等主流推理引擎,可无缝扩展,从单机环境平滑伸缩至大规模 GPU 集群。"
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"message": "基于 Ray 的分布式架构,支持 vLLM、SGLang、Megatron-Core 等主流训练推理引擎,可无缝扩展,从单机环境平滑伸缩至大规模 GPU 集群。"
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},
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"Multi-task Learning": {
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"message": "多任务学习"
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},
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"Commits": {
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"message": "提交"
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},
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"Follow us on X": {
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"message": "关注我们的 X 账号"
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}
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}

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