LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
Abstract
We present LLM-ABR, the first system that utilizes the generative capabilities of large language models (LLMs) to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics. Operating within a reinforcement learning framework, LLM-ABR empowers LLMs to design key components such as states and neural network architectures. We evaluate LLM-ABR across diverse network settings, including broadband, satellite, 4G, and 5G. LLM-ABR consistently outperforms default ABR algorithms.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Large Language Model Adaptation for Networking (2024)
- Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback (2024)
- Supervised Fine-Tuning as Inverse Reinforcement Learning (2024)
- FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models : (Invited Paper) (2024)
- Simple linear attention language models balance the recall-throughput tradeoff (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper