update

Author: hijkzzz

_posts/2025-04-18-openrlhf-vllm.md

@@ -15,11 +15,11 @@ To address these challenges, OpenRLHF is designed as a user-friendly, high-perfo
 
 **Ray** serves as the backbone for distributed programming within OpenRLHF. Its robust scheduling and orchestration capabilities make it ideal for managing the complex data flows and computations inherent in RLHF training, including the distribution of reward models across multiple nodes. 
 
-**vLLM with Ray Executor and Auto TP** is central to accelerating inference within OpenRLHF. It naturally supports Ray Executors and integrates with Hugging Face Transformers, enabling efficient weight updates through AutoTP. This combination ensures high-throughput, memory-efficient serving of large language models.
+**vLLM with Ray Executor and AutoTP** is central to accelerating inference within OpenRLHF. It naturally supports Ray Executors and integrates with Hugging Face Transformers, enabling efficient weight updates through AutoTP. This combination ensures high-throughput, memory-efficient serving of large language models.
 
 **ZeRO-3**, a memory optimization strategy from DeepSpeed, enables OpenRLHF to train large-scale models without the need for complex frameworks like Megatron. This allows for seamless integration with Hugging Face Transformers, facilitating straightforward loading and fine-tuning of pre-trained models. 
 
-By combining Ray, vLLM, ZeRO-3, and Hugging Face Transformers, OpenRLHF offers a leading solution for accelerating RLHF training. This architecture has influenced other frameworks, such as veRL, which adopt a similar paradigm for efficient and scalable RLHF training.
+By combining Ray, vLLM, ZeRO-3, and Hugging Face Transformers, OpenRLHF offers a leading and simple solution for accelerating RLHF training. This architecture has influenced other frameworks, such as veRL, which adopt a similar paradigm for efficient and scalable RLHF training.
 
 <img align="center" src="/assets/figures/openrlhf-vllm/ray.png" alt="Ray and vLLM in OpenRLHF" width="90%" height="90%">