Adversarial Training and Reinforcement Learning for Robust Reasoning in Large Language Models
Keywords:
adversarial training, reinforcement learning, large language models, robust reasoning, socio-technical systems, AI governance, fairness, deployment infrastructureAbstract
Large language models increasingly function as reasoning engines inside high-stakes socio-technical systems, yet their reasoning remains vulnerable to adversarial prompts, distributional shifts, spurious correlations, and reward hacking. This paper examines adversarial training and reinforcement learning as complementary system-level strategies for improving robust reasoning in large language models. It argues that robustness cannot be treated as a narrow model property, because it emerges from interactions among training pipelines, inference architecture, retrieval infrastructure, verification mechanisms, human feedback, deployment constraints, and governance regimes. Adversarial training strengthens local decision boundaries by exposing models to crafted or naturally occurring perturbations, but it can also produce overfitting to known attack families and impose substantial computational costs. Reinforcement learning, especially preference-based and verifier-guided optimization, can shape reasoning trajectories toward more reliable intermediate steps, yet it introduces risks of reward misspecification, mode collapse, evaluator capture, and unequal treatment of diverse populations. The paper therefore develops an integrated perspective that treats robust reasoning as an infrastructure problem requiring layered defenses, continuous red teaming, calibrated evaluation, transparent provenance, and adaptive policy controls. It discusses structural trade-offs among robustness, fairness, efficiency, interpretability, and sustainability, and it proposes governance principles for deploying adversarial and reinforcement-based training in public and private institutions. The conclusion emphasizes that robust reasoning should be assessed through system performance under uncertainty rather than through single benchmark scores, and that durable progress depends on aligning technical design with institutional accountability.
References
1. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. arXiv. https://arxiv.org/abs/1412.6572
2. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. In International Conference on Learning Representations.
3. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations.
4. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (pp. 39-57). IEEE.
5. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258
6. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://arxiv.org/abs/2303.12712
7. Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., ... Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv. https://arxiv.org/abs/2112.04359
8. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems (Vol. 30, pp. 4299-4307).
9. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... Lowe, R. (2022). Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems (Vol. 35, pp. 27730-27744).
10. Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., ... Kaplan, J. (2022). Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv. https://arxiv.org/abs/2204.05862
11. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv. https://arxiv.org/abs/1707.06347
12. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems (Vol. 35, pp. 24824-24837).
13. Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., & Cobbe, K. (2023). Let's verify step by step. arXiv. https://arxiv.org/abs/2305.20050
14. Li, Q. (2026, May). TrajLite PPO: Scalable Reasoning Path Filtering and Alignment for Small Parameter Large Language Models. In 2026 2nd International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) (pp. 29-32). IEEE.
15. Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping reasoning with reasoning. In Advances in Neural Information Processing Systems (Vol. 35, pp. 15476-15488).
16. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems (Vol. 35, pp. 22199-22213).
17. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you've signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (pp. 79-90). ACM.
18. Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., ... Irving, G. (2022). Red teaming language models with language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 3419-3448). Association for Computational Linguistics.
19. Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, A., Schaeffer, R., Truong, S. T., Arora, S., Mazeika, M., Hendrycks, D., Lin, Z., Cheng, Y., Koyejo, S., Song, D., & Li, B. (2023). DecodingTrust: A comprehensive assessment of trustworthiness in GPT models. In Advances in Neural Information Processing Systems (Vol. 36).
20. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv. https://arxiv.org/abs/1606.06565
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
