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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2601.09992 |
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| _version_ | 1866908767187107840 |
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| author | Xiao, Zhuoran Tao, Tao Ye, Chenhui Hu, Yunbo Feng, Yijia Jiao, Tianyu Cai, Liyu |
| author_facet | Xiao, Zhuoran Tao, Tao Ye, Chenhui Hu, Yunbo Feng, Yijia Jiao, Tianyu Cai, Liyu |
| contents | Owing to its comprehensive understanding of upper-layer application requirements and the capabilities of practical communication systems, the 6G-LLM (6G domain large language model) offers a promising pathway toward realizing network native intelligence. Serving as the system orchestrator, the 6G-LLM drives a paradigm shift that fundamentally departs from existing rule-based approaches, which primarily rely on modular, experience-driven optimization. By contrast, the 6G-LLM substantially enhances network flexibility and adaptability. Nevertheless, current efforts to construct 6G-LLMs are constrained by their reliance on large-scale, meticulously curated, human-authored corpora, which are impractical to obtain in real-world scenarios. Moreover, purely offline-trained models lack the capacity for continual self-improvement, limiting their ability to adapt to the highly dynamic requirements of wireless communication environments. To overcome these limitations, we propose a novel training paradigm termed RLDTF (Reinforcement Learning from Digital Twin Feedback) for 6G-LLMs. This framework leverages network digital twins to generate reward signals based on orchestration outcomes, while employing reinforcement learning to guide the model toward optimal decision-making dynamically. Furthermore, we introduce a weighted token mechanism to improve output accuracy. Comprehensive experimental results demonstrate that our proposed framework significantly outperforms state-of-the-art baselines in orchestration accuracy and solution optimality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09992 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Native Intelligence: 6G-LLM Trained with Reinforcement Learning from NDT Feedback Xiao, Zhuoran Tao, Tao Ye, Chenhui Hu, Yunbo Feng, Yijia Jiao, Tianyu Cai, Liyu Signal Processing Owing to its comprehensive understanding of upper-layer application requirements and the capabilities of practical communication systems, the 6G-LLM (6G domain large language model) offers a promising pathway toward realizing network native intelligence. Serving as the system orchestrator, the 6G-LLM drives a paradigm shift that fundamentally departs from existing rule-based approaches, which primarily rely on modular, experience-driven optimization. By contrast, the 6G-LLM substantially enhances network flexibility and adaptability. Nevertheless, current efforts to construct 6G-LLMs are constrained by their reliance on large-scale, meticulously curated, human-authored corpora, which are impractical to obtain in real-world scenarios. Moreover, purely offline-trained models lack the capacity for continual self-improvement, limiting their ability to adapt to the highly dynamic requirements of wireless communication environments. To overcome these limitations, we propose a novel training paradigm termed RLDTF (Reinforcement Learning from Digital Twin Feedback) for 6G-LLMs. This framework leverages network digital twins to generate reward signals based on orchestration outcomes, while employing reinforcement learning to guide the model toward optimal decision-making dynamically. Furthermore, we introduce a weighted token mechanism to improve output accuracy. Comprehensive experimental results demonstrate that our proposed framework significantly outperforms state-of-the-art baselines in orchestration accuracy and solution optimality. |
| title | Towards Native Intelligence: 6G-LLM Trained with Reinforcement Learning from NDT Feedback |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.09992 |