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docs/tutorial/gsm8k_grpo.md

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NCCL AllReduce for gradients
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```
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Each worker processes its shard, then synchronizes gradients via NCCL. For custom
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algorithms, see
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[Customization: Algorithms](https://inclusionai.github.io/AReaL/customization/algorithm.html).
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Each worker processes its shard, then synchronizes gradients via NCCL.
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### The Training Loop
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- [Evaluation](../tutorial/eval.md) - Evaluate your trained model
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- [Training Large MoE Models](../tutorial/megatron.md) - Scale to massive models with
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Megatron integration
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- [Agentic RL with OpenAI APIs](../tutorial/agentic_rl.md) - Build agents that use tools
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and APIs
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- [Agentic RL](../tutorial/agentic_rl.md) - Build agents that use tools and any agentic
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frameworks
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**Customization Guides**:
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- [Custom Datasets](../customization/dataset.md) - Use your own data sources
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- [Custom Workflows](../customization/agent.md) - Build agentic/RLVR workflows with
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custom reward functions
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- [Custom Algorithms](../customization/algorithm.md) - Implement your own RL algorithms

docs/tutorial/quickstart.md

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- [Custom dataset](../customization/dataset.md)
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- [Custom agentic/RVLR rollout workflows](../customization/agent.md)
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- [Custom algorithms](../customization/algorithm.md)

examples/tau2/train.py

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from examples.tau2.utils import Tau2PPOConfig
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from areal import PPOTrainer
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from areal.api.cli_args import load_expr_config
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from areal.experimental.trainer.rl import PPOTrainer
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from areal.utils import logging
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logger = logging.getLogger("Tau2Train")

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