# Reinforcement-Learned Unequal Error Protection for Quantized Semantic Embeddings

arXiv: https://arxiv.org/abs/2601.00186 · HTML: https://mlsanity.com/paper/2601.00186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 1 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Deep Joint Source-Channel Coding for Wireless Image Transmission](https://mlsanity.com/paper/1809.01733/) (2018)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [FoldToken: Learning Protein Language via Vector Quantization and Beyond](https://mlsanity.com/paper/2403.09673/) (2024)
- [A Survey on Semantic Communication Networks: Architecture, Security, and Privacy](https://mlsanity.com/paper/2405.01221/) (2024)
- [Embedding Compression with Isotropic Iterative Quantization](https://mlsanity.com/paper/2001.05314/) (2020)
- [A Survey of Secure Semantic Communications](https://mlsanity.com/paper/2501.00842/) (2025)
- [LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesis](https://mlsanity.com/paper/2511.06000/) (2025)
- [4bit-Quantization in Vector-Embedding for RAG](https://mlsanity.com/paper/2501.10534/) (2025)
- [LLM-Enabled Data Transmission in End-to-End Semantic Communication](https://mlsanity.com/paper/2504.07431/) (2025)
- [Deep Reinforcement Learning-Based Resource Allocation for Hybrid Bit and Generative Semantic Communications in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2412.05647/) (2024)
- [Unequal Error Protection for Digital Semantic Communication with Channel Coding](https://mlsanity.com/paper/2508.03381/) (2025)

## Abstract (arXiv)

This paper tackles the pressing challenge of preserving semantic meaning in communication systems constrained by limited bandwidth. We introduce a novel reinforcement learning framework that achieves per-dimension unequal error protection via adaptive repetition coding. Central to our approach is a composite semantic distortion metric that balances global embedding similarity with entity-level preservation, empowering the reinforcement learning agent to allocate protection in a context-aware manner. Experiments show statistically significant gains over uniform protection, achieving 6.8% higher chrF scores and 9.3% better entity preservation at 1 dB SNR. The key innovation of our framework is the demonstration that simple, intelligently allocated repetition coding enables fine-grained semantic protection -- an advantage unattainable with conventional codes such as LDPC or Reed-Solomon. Our findings challenge traditional channel coding paradigms by establishing that code structure must align with semantic granularity. This approach is particularly suited to edge computing and IoT scenarios, where bandwidth is scarce, but semantic fidelity is critical, providing a practical pathway for next-generation semantic-aware networks.