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arXiv · 2511.03939

RLHF: A comprehensive Survey for Cultural, Multimodal and Low Latency Alignment Methods

Raghav Sharma·Manan Mehta·Sai Tiger Raina
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Abstract

Reinforcement Learning from Human Feedback (RLHF) is the standard for aligning Large Language Models (LLMs), yet recent progress has moved beyond canonical text-based methods. This survey synthesizes the new frontier of alignment research by addressing critical gaps in multi-modal alignment, cultural fairness, and low-latency optimization. To systematically explore these domains, we first review foundational algo- rithms, including PPO, DPO, and GRPO, before presenting a detailed analysis of the latest innovations. By providing a comparative synthesis of these techniques and outlining open challenges, this work serves as an essential roadmap for researchers building more robust, efficient, and equitable AI systems.

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Raghav Sharma, Manan Mehta, Sai Tiger Raina. 2025-11-06. RLHF: A comprehensive Survey for Cultural, Multimodal and Low Latency Alignment Methods. https://arxiv.org/abs/2511.03939

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