A Technical Survey of Reinforcement Learning Techniques for Large Language Models

2025-07-05

AI Summary

This work systematically investigates reinforcement learning (RL)-driven alignment and capability enhancement of large language models (LLMs), addressing three core challenges: instruction following, ethical compliance, and complex reasoning. Methodologically, it introduces a two-dimensional classification framework grounded in reward modeling and policy optimization to unify the analysis of prominent paradigms—including RLHF, DPO, RLAIF, GRPO, and RLVR. Empirical analysis reveals emerging trends: RLHF excels at foundational alignment, while RLVR significantly improves stepwise reasoning. The study identifies critical bottlenecks—reward gaming, multi-objective trade-offs, and computational overhead—and proposes novel directions: hybrid RL architectures and verifier-guided training. Collectively, this work delivers a principled technical roadmap and methodological foundation for developing safe, reliable, and scalable RL-augmented LLMs.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsComputer Vision: Large Vision Models

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Large language models for searchSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reinforcement Learning (RL) has emerged as a transformative approach for aligning and enhancing Large Language Models (LLMs), addressing critical challenges in instruction following, ethical alignment, and reasoning capabilities. 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). We systematically analyze their applications across domains, i.e., from code generation to tool-augmented reasoning. We also present a comparative taxonomy based on reward modeling, feedback mechanisms, and optimization strategies. Our evaluation highlights key trends. RLHF remains dominant for alignment, and outcome-based RL such as RLVR significantly improves stepwise reasoning. However, persistent challenges such as reward hacking, computational costs, and scalable feedback collection underscore the need for continued innovation. We further discuss emerging directions, including hybrid RL algorithms, verifier-guided training, and multi-objective alignment frameworks. This survey serves as a roadmap for researchers advancing RL-driven LLM development, balancing capability enhancement with safety and scalability.

Problem

• Aligning and enhancing LLMs using RL techniques
• Addressing challenges in instruction following and ethical alignment
• Improving reasoning capabilities and scalability of LLMs

Innovation

• Integrates RL with LLMs using PPO and Q-Learning
• Employs RLHF and RLAIF for human and AI feedback
• Uses DPO and GRPO for advanced optimization strategies