![[2602.22718] RLHFless: Serverless Computing for Efficient RLHF](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22718] RLHFless: Serverless Computing for Efficient RLHF
Summary
The paper introduces RLHFless, a serverless computing framework designed to enhance the efficiency of Reinforcement Learning from Human Feedback (RLHF) by addressing resource variability and reducing costs.
Why It Matters
As AI models grow in complexity, optimizing their training processes becomes crucial. RLHFless offers a novel approach to improve training efficiency in RLHF, potentially transforming how large language models are developed and deployed, making them more cost-effective and faster.
Key Takeaways
- RLHFless is the first scalable framework for synchronous RLHF using serverless computing.
- The framework adapts to dynamic resource demands, reducing idle time and improving cost efficiency.
- Experiments show RLHFless achieves up to 1.35x speedup and 44.8% cost reduction compared to existing methods.
Computer Science > Artificial Intelligence arXiv:2602.22718 (cs) [Submitted on 26 Feb 2026] Title:RLHFless: Serverless Computing for Efficient RLHF Authors:Rui Wei, Hanfei Yu, Shubham Jain, Yogarajan Sivakumar, Devesh Tiwari, Jian Li, Seung-Jong Park, Hao Wang View a PDF of the paper titled RLHFless: Serverless Computing for Efficient RLHF, by Rui Wei and 7 other authors View PDF HTML (experimental) Abstract:Reinforcement Learning from Human Feedback (RLHF) has been widely applied to Large Language Model (LLM) post-training to align model outputs with human preferences. Recent models, such as DeepSeek-R1, have also shown RLHF's potential to improve LLM reasoning on complex tasks. In RL, inference and training co-exist, creating dynamic resource demands throughout the workflow. Compared to traditional RL, RLHF further challenges training efficiency due to expanding model sizes and resource consumption. Several RLHF frameworks aim to balance flexible abstraction and efficient execution. However, they rely on serverful infrastructures, which struggle with fine-grained resource variability. As a result, during synchronous RLHF training, idle time between or within RL components often causes overhead and resource wastage. To address these issues, we present RLHFless, the first scalable training framework for synchronous RLHF, built on serverless computing environments. RLHFless adapts to dynamic resource demands throughout the RLHF pipeline, pre-computes shared prefixes to avoi...
