The Role of Reward Models and Reinforcement Learning in LLM Fine-tuning

Authors

  • Venkata Bharathula Siva Prasad Bharathula University of Florida, USA Author

DOI:

https://doi.org/10.32628/CSEIT25112381

Keywords:

Reward Models, Reinforcement Learning from Human Feedback, Constitutional AI, Language Model Fine-tuning, Human Preference Alignment

Abstract

This comprehensive article explores the evolution and implementation of reward models and reinforcement learning techniques in fine-tuning Large Language Models (LLMs). The article examines the fundamental role of reward models in capturing human preferences, the methodological approaches to training these models, and the integration of Reinforcement Learning from Human Feedback (RLHF) in model optimization. It discusses recent advances in Constitutional AI and optimization algorithms, while highlighting current challenges in reward model robustness, scalability, and preference learning. The review analyzes various training approaches, their effectiveness, and the trade-offs involved in different implementation strategies, providing insights into future directions for improving LLM alignment with human preferences.

Downloads

Download data is not yet available.

References

Long Ouyang et al., “Training language models to follow instructions with human feedback”, arXiv:2203.02155, 2022. Available: https://arxiv.org/abs/2203.02155

Paul Christiano et al., “Deep reinforcement learning from human preferences." arXiv:1706.03741, 2017. Available: https://arxiv.org/abs/1706.03741

Nisan Stiennon et al., “Learning to summarize from human feedback,” arXiv preprint arXiv:1909.08593, 2020. Available: https://arxiv.org/abs/2009.01325

Yuntao Bai et al., “Constitutional AI: Harmlessness from AI Feedback,” arXiv:2212.08073v1 [cs.CL], 2022. Available: https://arxiv.org/pdf/2212.08073

Dominic Petrak et al., “Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues,”. arXiv:2401.09248v2, 2024. Available: https://arxiv.org/html/2401.09248v2

Daniel M. Ziegler et al., “Fine-Tuning Language Models from Human Preferences”, Computation and Language, 2020. Available: https://arxiv.org/abs/1909.08593

Tom B. Brown et al., “Language Models are Few-Shot Learners”, arXiv:2005.14165. 2020. Available: https://arxiv.org/abs/2005.14165

John Schulman et al., “Proximal Policy Optimization Algorithms”, arXiv preprint arXiv:1707.06347, 2017. Available: https://arxiv.org/abs/1707.06347

Shengnan Han et al., “Aligning artificial intelligence with human values: reflections from a phenomenological perspective,” AI & SOCIETY, 2021. Available: https://www.researchgate.net/publication/353355501_Aligning_artificial_intelligence_with_human_values_reflections_from_a_phenomenological_perspective

Akshit Mehra, “Complete Guide On Fine-Tuning LLMs using RLHF", Labellerr Insights, 2024. Available: https://www.labellerr.com/blog/reinforcement-learning-from-human-feedback/

Yuzi Yan et al., “Reward-Robust RLHF in LLMs,” arXiv:2409.15360, 2024. Available: https://arxiv.org/abs/2409.15360

Deepak Narayanan et al., “Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM”, arXiv:2104.04473, 2021. Available: https://arxiv.org/abs/2104.04473

Muhammad Usman Hadi et al., “Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects”, ResearchGate, 2024. Available: https://www.researchgate.net/publication/383058502_Large_Language_Models_A_Comprehensive_Survey_of_its_Applications_Challenges_Limitations_and_Future_Prospects

Downloads

Published

04-03-2025

Issue

Section

Research Articles