Keywords
Latent Representations
Mutual Information
Policy Updates
Reward Alignment
Training Stability
Abstract
Latent Policy Optimization depends on a defensible relationship between performance with evidence quality, resource limits, and transfer across settings. The review develops an evidence-centered account of controlling latent information flow to improve optimization stability, exploration, and interpretability. The evidence base combines 1 focal paper with 13 independently retrieved publications whose DOI or publisher records were checked before inclusion. The analysis is organized around latent representations, mutual information, policy updates, reward alignment, and training stability. The synthesis resists treating results from heterogeneous studies as exchangeable, the review compares study questions, technical premises, and validation scope. Across the literature, the comparison suggests that advances in latent policy optimization become credible when representation, objective, and evaluation protocol are evaluated together and when uncertainty about distribution shift is reported explicitly. This organization relates method selection to operational consequence while identifying external-validity hazards, and proposes a research agenda centered on comparable baselines, sensitivity analysis, and retained provenance.
References
Deng, H., Luo, H., Zhu, Y., Li, L., Chen, Z., Zhao, X., ... & Kang, Y. (2026, July). I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 23647-23664).
You, B., & Liu, H. (2024). Multimodal information bottleneck for deep reinforcement learning with multiple sensors. Neural Networks, 176, 106347. https://doi.org/10.1016/j.neunet.2024.106347
Xi, R., Ni, Y., & Wu, W. (2025). Information Bottleneck-Enhanced Reinforcement Learning for Solving Operation Research Problems. Sensors, 25(24), 7572. https://doi.org/10.3390/s25247572
Wang, D., He, J., Wang, X., & Li, Z. (2025). Sensor activation policy optimization for K-diagnosability based on multi-agent reinforcement learning. Information Sciences, 718, 122360. https://doi.org/10.1016/j.ins.2025.122360
Yang, Z., Li, G., & Xue, Y. (2026). Information Bottleneck for Communication-Efficient Multi-Agent Reinforcement Learning in UAV Swarms. Entropy, 28(8), 919. https://doi.org/10.3390/e28080919
Chen, X., Yang, M., Meng, H., Tian, S., & Wang, Z. (2026). Maximum information gain reinforcement learning based on the variational information bottleneck. Physical Communication, 80, 103332. https://doi.org/10.1016/j.phycom.2026.103332
Zhang, S., Wang, Y., Liu, X., & Ji, Z. (2025). Model-free guiding of Boolean control networks: Reinforcement learning and adversarial optimization. Information Sciences, 721, 122576. https://doi.org/10.1016/j.ins.2025.122576
Xiang, G., Dian, S., Du, S., & Lv, Z. (2023). Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation. Sensors, 23(2), 762. https://doi.org/10.3390/s23020762
Kaczmarek, A. (2026). Secure Routing in Software-Defined Networks via Proximal Policy Optimization-Based Deep Reinforcement Learning. Journal of Intelligent Information and Communication, 4, 1-13. https://doi.org/10.64972/jiic.2026v4.110p1-13
Zou, Q., & Suzuki, E. (2025). Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems, 36(2), 2368-2381. https://doi.org/10.1109/tnnls.2023.3344880
Hadizadeh Moghaddam, A., & Ghayoomi, M. (2023). Language independent optimization of text readability formulas with deep reinforcement learning. Information Design Journal, 28(1), 33-52. https://doi.org/10.1075/idj.22015.had
Öztürk, M. M. (2024). MFRLMO: Model-free reinforcement learning for multi-objective optimization of apache spark. ICST Transactions on Scalable Information Systems, 11(5). https://doi.org/10.4108/eetsis.4764
Jyoti Shah, Prashanthi Matam, (2025). Integration Online Reinforcement Learning Loops in Language Model Training. Journal of Information Systems Engineering and Management, 9(4s), 1017-1026. https://doi.org/10.52783/jisem.v9i4s.11627
Li, J., Yang, Y., He, Z., Wu, H., Shi, H., & Chen, W. (2024). Cournot Policy Model: Rethinking centralized training in multi-agent reinforcement learning. Information Sciences, 677, 120983. https://doi.org/10.1016/j.ins.2024.120983
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Arthur Crawford, Lawrence Lawson, Mark Johnston (Author)