Refining Decision Boundaries via Stepwise Reinforcement Learning from Human Feedback Integrating Intermediate Logic Verification and Large Language Model Reasoning
DOI:
https://doi.org/10.66280/ijair.v1i2.152Keywords:
Reinforcement Learning from Human Feedback, Stepwise Reasoning, Logic Verification, Socio-Technical Systems, Decision Boundaries, Large Language Models.Abstract
The evolution of generative artificial intelligence has transitioned from simple sequence prediction to complex multi-step reasoning, necessitating more granular control mechanisms over model behavior. While Reinforcement Learning from Human Feedback has historically optimized models based on holistic outcome-based rewards, this approach often fails to address the "black box" nature of intermediate logic, leading to correct answers derived from flawed reasoning. This paper proposes a system-level framework for refining decision boundaries through Stepwise Reinforcement Learning from Human Feedback. By integrating intermediate logic verification with large language model reasoning, the proposed architecture shifts the evaluative focus from terminal states to incremental transitions. We analyze the structural trade-offs between computational overhead and logical fidelity, emphasizing the necessity of verifiable reasoning traces in high-stakes socio-technical infrastructures. Our discussion extends to the governance and policy implications of such systems, exploring how stepwise verification enhances robustness, fairness, and accountability. The research demonstrates that by decomposing complex tasks into verifiable logical units, organizations can mitigate the risks of hallucination and reward hacking while ensuring that AI systems remain aligned with human-centric ethical standards and operational constraints.
References
1.Ahn, J. K., Kim, S., & Lee, H. (2021). Building trust through outcome feedback in human-AI collaboration. Journal of Human-Computer Interaction, 15(2), 112–125.
2.Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., ... & Kaplan, J. (2024). Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073.
3.BaniHani, A., & Buijsman, S. (2024). Trust and transparency in automated decision systems. AI and Ethics, 4(1), 45–58.
4.Cabinet Office. (2022). Government Digital Service: Data standards for public sector projects. UK Government Publishing Service.
5.Chen, L., Wang, Y., & Zhang, R. (2025). Learning to generate formally verifiable step-by-step logic reasoning via structured formal intermediaries. arXiv preprint arXiv:2603.29500.
6.Cited by: 2
7.Chen, B., et al. (2025). The risks of outcome-only rewards in mathematical reasoning. Journal of Artificial Intelligence Research, 72, 412–435.
8.Denti, L., & Hemlin, S. (2012). Leadership and innovation in organizations: A systematic review of factors that mediate or moderate the relationship. International Journal of Innovation Management, 16(03), 1250015.
9.Dou, Z., Cui, D., Yan, J., Wang, W., Chen, B., Wang, H., ... & Zhang, S. (2025). Dsadf: Thinking fast and slow for decision making. arXiv preprint arXiv:2505.08189.
10.Fürst, J. (2025). The experts know it all: Reinforcement learning from human feedback for legal information extraction. KTH Royal Institute of Technology.
11.Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660.
12.Guo, Z., et al. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948.
13.Haupt, J., et al. (2025). Explainable AI in high-stakes strategic decision making. Strategic Management Journal, 46(4), 889–910.
14.Huynh, T., & Aichner, T. (2025). Transparency and user trust in generative AI applications. Computers in Human Behavior, 162, 108421.
15.Jaech, A., et al. (2024). OpenAI o1: Scaling laws for reasoning. OpenAI Technical Report.
16.Jarrahi, M. H. (2018). Artificial intelligence and the future of work: A human-AI symbiosis. Business Horizons, 61(4), 577–586.
17.Kleinberg, J., Ludwig, J., Mullainathan, S., & Rambachan, A. (2020). Algorithmic decisions and the law. The University of Chicago Law Review, 87(2), 471-502.
18.Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.
19.Kumar, V., et al. (2025). Strategic framing of AI adoption in multinational corporations. Journal of World Business, 60(1), 101589.
20.Lazaros, K. (2026). Human-in-the-loop artificial intelligence: A systematic review of concepts, methods, and applications. MDPI Entropy, 28(4), 377.
21.Cited by: 5
22.Lightman, H., et al. (2024). Let’s verify step by step. arXiv preprint arXiv:2305.20050.
23.Lin, X. (2026). Making chatbots more human: Deep reasoning large language models in ophthalmology. Frontiers in Medicine, 13.
24.Liu, J., et al. (2024). Evaluating the calibration of reasoning models in medical diagnosis. Nature Machine Intelligence, 6(3), 245–258.
25.Martín-Urcelay, B. (2026). From words to rewards: Leveraging natural language for reinforcement learning. ETH Zurich Research Collection.
26.Mei, Z. (2026). Reasoning about uncertainty: Do reasoning models know when they don’t know? Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 145–160.
27.Cited by: 24
28.Merler, M. (2025). Guiding reinforcement learning with selective vision-language model supervision. CEUR Workshop Proceedings, 4103.
29.Pan, P. C. (2026). Reward modeling for reinforcement learning-based LLM reasoning: Design, challenges, and evaluation. arXiv preprint arXiv:2602.09305.
30.Cited by: 2
31.Qureshi, J. (2026). The socio-technical gap: An AI framework for project resilience in UK construction. Frontiers in the Built Environment, 12.
32.Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
33.Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
34.Saup, T. O. (2025). From pilots to decision systems: Embedding generative AI into strategic decision-making through a socio-technical and governance lens. Journal of Decision Systems, 34(2), 1–28.
35.Cited by: 9
36.Snell, C., et al. (2024). Scaling laws for test-time compute in large language models. arXiv preprint arXiv:2408.03314.
37.Uesato, J., et al. (2022). Solving math word problems with process-based feedback. arXiv preprint arXiv:2211.14275.
38.Wang, X., et al. (2022). Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171.
39.Wei, J., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
40.Xie, Y., et al. (2025). Logical RL: Strengthening multi-step reasoning through verifiable reward signals. International Conference on Learning Representations.
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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.
