Reinforcement Learning with Human Feedback (RLHF): Shaping the Future of AI Alignment Roadmap 2025-2035
Year: 2025
Type: preprint
Abstract: We present a comprehensive survey and roadmap for Reinforcement Learning with Human Feedback (RLHF) and closely related methods (DPO, RRHF, Constitutional AI). We unify practical training recipes (SFT → reward modeling → PPO-style optimization), discuss failure modes (reward hacking, KL instabilities), and evaluate data-efficiency trade-offs using reproducible synthetic experiments. We include a managerial roadmap (2025-2035) for scaling human and AI feedback, and cite key works with DOIs.
Author: Raushan Kumar Mahaseth
Institution: PayPal (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414520407