arXiv:2507.04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods. Additionally, it provides an extensive technical overview of RL techniques specifically tailored for LLMs, including foundational methods like Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), as well as advanced strategies such as Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO)
The rapid advancement and widespread adoption of Large Language Models necessitate increasingly sophisticated alignment and optimization techniques, making this survey timely.
A strategic reader should care because improved reinforcement learning methods directly enhance LLM capabilities, reliability, and safety, impacting diverse applications and the future of AI.
This detailed technical overview provides a consolidated resource that will likely accelerate research and practical application of advanced RL in LLMs by highlighting key methods and challenges.
- · AI research labs
- · LLM developers
- · Companies using AI for automation
- · Companies relying on outdated LLM training methods
The survey consolidates knowledge on current cutting-edge RL techniques for LLMs, making these methods more accessible to researchers.
Accelerated development and more robust deployment of advanced AI agents will likely follow as these techniques become more widely understood and implemented.
This could lead to a significant improvement in the trustworthiness and generalizability of AI systems, potentially broadening their integration into critical sectors.
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Read at arXiv cs.AI