Reinforcement Learning from Human and AI Feedback for Large Language Model Alignment: A Review
DOI:
https://doi.org/10.63503/j.ijssic.2026.234Keywords:
reinforcement learning, LLMs(Large Language Model), rlhf, rlaif, human feedbackAbstract
Safe and effective deployment of AI requires that large language models (LLMs) generate it in a way that complies with human values and preferences. The application of Reinforcement Learning from Human Feedback (RLHF) has effectively been applied to fine-tune models on human judgment-based categories, enhancing the helpfulness, coherence, and safety. Nonetheless, RLHF suffers certain limitations, such as using high-quality human labels, being costly, slow to iterate, and not being consistent owing to the subjectivity of annotators. The Reinforcement Learning AI Feedback (RLAIF) has become a scalable and effective method of resolving the challenges. The RLAIF provides an opportunity to use AI-generated preferences, revisions, and reward modeling to automatically fine-tune LLMs without violating ethical and safety standards. This will decrease human efforts, enhance reproducibility, and enhance response harmlessness, uniformity, and ethical compliance. Applications of RLAIF have been successful in dialogue generation, summarization, content personalization and automated reasoning. The review summarizes the recent research of feedback-based reinforcement learning, including underlying mechanisms, practical advantages, constraints, and usage of RLAIF. It points out that AI-based feedback offers a systematic and scalable channel of enhancing alignment, robustness and safety of large-scale language models.
References
[1] D. Bill and T. Eriksson, “Fine-tuning a LLM using reinforcement learning from human feedback for a therapy chatbot application,” 2023. :contentReference[oaicite:0]{index=0}
[2] S. Achouche, U. B. Yalamanchi, and N. Raveendran, “Method, apparatus, and computer-readable medium for performing a data exchange on a data exchange framework,” U.S. Patent 10,387,195 B2, 2019.
[3] R. Guha, “Fine-tuning human for LLM projects,” SSRN, 2023.
[4] Y. Dubois et al., “AlpacaFarm: A simulation framework for methods that learn from human feedback,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 36, pp. 30039–30069, 2023.
[5] V. Pal, “Bias detection and mitigation in foundation AI models: A human-centric approach,” TIJER – Int. Res. J., vol. 8, no. 2, pp. 1–7, 2021.
[6] S. K. Chintagunta, “AI in code, testing, and deployment: A survey on productivity enhancement in modern software engineering,” Int. J. Res. Anal. Rev., vol. 10, no. 4, pp. 747–752, 2023.
[7] S. Thangavel, S. Srinivasan, S. B. V. Naga, and K. Narukulla, “Distributed machine learning for big data analytics: Challenges, architectures, and optimizations,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 4, no. 3, pp. 18–30, Oct. 2023, doi: 10.63282/3050-9262.IJAIDSML-V4I3P103.
[8] H. R. Kirk, A. M. Bean, B. Vidgen, P. Röttger, and S. A. Hale, “The past, present and better future of feedback learning in large language models for subjective human preferences and values,” arXiv preprint arXiv:2310.07629, 2023.
[9] C.-A. Cheng, A. Kolobov, D. Misra, A. Nie, and A. Swaminathan, “LLF-Bench: Benchmark for interactive learning from language feedback,” arXiv preprint arXiv:2312.06853, 2023.
[10] D. Patel, “AI-enhanced natural language processing for improving web page classification accuracy,” ESP J. Eng. Technol. Adv., vol. 4, no. 1, 2024, doi: 10.56472/25832646/JETA-V4I1P119.
[11] E. J. Hu et al., “LoRA: Low-rank adaptation of large language models,” in Proc. Int. Conf. Learn. Representations (ICLR), 2022.
[12] S. Paul and S. Manglukar, “PEFT: Parameter-efficient fine-tuning of billion-scale models on low-resource hardware,” Feb. 2023.
[13] Vaswani et al., “Attention is all you need,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2017.
[14] Y. Du et al., “Guiding pretraining in reinforcement learning with large language models,” in Proc. Int. Conf. Mach. Learn. (ICML), 2023, pp. 8657–8677.
[15] T. Carta et al., “Grounding large language models in interactive environments with online reinforcement learning,” in Proc. Int. Conf. Mach. Learn. (ICML), 2023, pp. 3676–3713.
[16] S. Huang, J. Zhao, Y. Li, and L. Wang, “Learning preference model for LLMs via automatic preference data generation,” in Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP), 2023, pp. 9187–9199.
[17] H. Bansal, J. Dang, and A. Grover, “Peering through preferences: Unraveling feedback acquisition for aligning large language models,” arXiv preprint arXiv:2308.15812, 2023.
[18] R. Zheng et al., “Secrets of RLHF in large language models part I: PPO,” arXiv preprint arXiv:2307.04964, 2023.
[19] G. Sarraf, “DeepDefender: High-precision network threat classification using adversarial-resistant neural networks,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 2, no. 1, pp. 596–606, 2022, doi: 10.48175/IJARSCT-3600E.
[20] S. Casper et al., “Open problems and fundamental limitations of reinforcement learning from human feedback,” arXiv preprint arXiv:2307.15217, 2023.
[21] M. Bakker et al., “Fine-tuning language models to find agreement among humans with diverse preferences,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 35, pp. 38176–38189, 2022.
[22] R. Kanchana, K. Phusavat, Z. Pastuszak, A. N. Hidayanto, and J. Majava, “Effects of external feedback on disengagement in a human-centric environment,” Hum. Syst. Manag., vol. 41, no. 6, pp. 685–697, 2022.
[23] J. Abramson et al., “Improving multimodal interactive agents with reinforcement learning from human feedback,” arXiv preprint arXiv:2211.11602, 2022.
[24] S. Höglund and J. Khedri, “Comparison between RLHF and RLAIF in fine-tuning a large language model,” 2023.
[25] K. M. R. Seetharaman and S. Pandya, “Importance of artificial intelligence in transforming sales, procurement, and supply chain processes,” Int. J. Recent Technol. Sci. Manag., vol. 8, no. 7, pp. 1–9, 2023.
[26] V. Gallego, “ZYN: Zero-shot reward models with yes-no questions for RLAIF,” arXiv preprint arXiv:2308.06385, 2023.
[27] G. K.-M. Liu, “Transforming human interactions with AI via reinforcement learning with human feedback (RLHF),” Massachusetts Institute of Technology, 2023.
[28] R. Saxena, S. A. Pushkala, and R. Carvalho, “Systems and methods for rapid processing of file data,” U.S. Patent 9,594,817, Mar. 2017.
[29] H. P. Kapadia, “Generative AI for real-time conversational agents,” Int. J. Curr. Sci., vol. 13, no. 3, pp. 201–208, 2023.
[30] M. Abdullah, A. Madain, and Y. Jararweh, “ChatGPT: Fundamentals, applications and social impacts,” in Proc. 9th Int. Conf. Social Netw. Anal., Manag. Security (SNAMS), IEEE, 2022, pp. 1–8, doi: 10.1109/SNAMS58071.2022.10062688.
[31] V. Verma, “Security compliance and risk management in AI-driven financial transactions,” Int. J. Eng. Sci. Math., vol. 12, no. 7, pp. 1–15, 2023.
[32] Y. Bai et al., “Constitutional AI: Harmlessness from AI feedback,” 2022.
[33] S. Garg, “AI-driven innovations in storage quality assurance and manufacturing optimization,” Int. J. Multidiscip. Res. Growth Eval., vol. 1, no. 1, pp. 143–147, 2020, doi: 10.54660/IJMRGE.2020.1.1.143-147.
[34] W. Shen et al., “Improving reinforcement learning from human feedback using contrastive rewards,” 2024.
[35] H. Lee et al., “RLAIF: Scaling reinforcement learning from human feedback with AI feedback,” 2023.
[36] D. Jin et al., “Data-efficient alignment of large language models with human feedback through natural language,” 2023.
[37] L. Ouyang et al., “Training language models to follow instructions with human feedback,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2022.
[38] Y. Bai et al., “Training a helpful and harmless assistant with reinforcement learning from human feedback,” 2022.
[39] K. Jain, “Advancements in large-scale language models for personalization,” Int. J. Comput. Technol. Electron. Commun., 2021.
