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[doc] fix: colocation documentation updates (#2465)

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### What does this PR do?

Update docs and awesome work.
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2025-07-10 17:11:17 -07:00
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README.md
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@ -214,6 +214,8 @@ verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The
- [ReTool](https://retool-rl.github.io/): ReTool: reinforcement learning for strategic tool use in LLMs. Code release is in progress...
- [verl-tool](https://github.com/TIGER-AI-Lab/verl-tool): An unified and easy-to-extend tool-agent training framework based on verl![GitHub Repo stars](https://img.shields.io/github/stars/TIGER-AI-Lab/verl-tool)
- [PRIME](https://github.com/PRIME-RL/PRIME): Process reinforcement through implicit rewards ![GitHub Repo stars](https://img.shields.io/github/stars/PRIME-RL/PRIME)
- [MemAgent](https://github.com/BytedTsinghua-SIA/MemAgent): MemAgent: Reshaping Long-Context LLM with Multi-Conv RL based Memory Agent ![GitHub Repo stars](https://img.shields.io/github/stars/BytedTsinghua-SIA/MemAgent)
- [POLARIS](https://github.com/ChenxinAn-fdu/POLARIS): A Post-training recipe for scaling RL on Advanced Reasoning models ![GitHub Repo stars](https://img.shields.io/github/stars/ChenxinAn-fdu/POLARIS)
- [GUI-R1](https://github.com/ritzz-ai/GUI-R1): **GUI-R1**: A Generalist R1-style Vision-Language Action Model For **GUI Agents** ![GitHub Repo stars](https://img.shields.io/github/stars/ritzz-ai/GUI-R1)
- [DeepRetrieval](https://github.com/pat-jj/DeepRetrieval): RL Training of **Search Agent** with **Search/Retrieval Outcome** ![GitHub Repo stars](https://img.shields.io/github/stars/pat-jj/DeepRetrieval)
- [Code-R1](https://github.com/ganler/code-r1): Reproducing R1 for **Code** with Reliable Rewards ![GitHub Repo stars](https://img.shields.io/github/stars/ganler/code-r1)

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docs/hybrid_flow.rst
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@ -117,7 +117,7 @@ Each worker inside the WorkerGroup runs on a GPU. The worker group serves as a p
For example, in PPO, we define 3 worker groups:
- ActorRolloutRef: manages actor, rollout and reference policy. ActorRolloutRefWorker can be instantiated as a single actor, a single rollout, a single reference policy, a combined actor/rollout or a combined actor/rollout/ref. This design is aimed for the maximum code reuse in various scenarios. The reason for colocating actor and rollout is for fast weight transfer using nccl. The reason for coloating actor and reference is to implement an efficient lora PPO as the reference policy is simply the base model of PPO in lora.
- ActorRolloutRef: manages actor, rollout and reference policy. ActorRolloutRefWorker can be instantiated as a single actor, a single rollout, a single reference policy, a combined actor/rollout or a combined actor/rollout/ref. This design is aimed for the maximum code reuse in various scenarios. The reason for colocating actor and rollout is for fast weight transfer using nccl. The reason for coloating actor and reference is to implement an efficient lora PPO as the reference policy is simply the base model of PPO in lora. The colocation is done via ``verl.single_controller.ray.base.create_colocated_worker_cls``, where it creates a single ray remote class exposing all class methods from these roles.
- Critic: manages the critic model
- Reward: manages the reward model