A Technical Survey of Reinforcement Learning Techniques for Large Language Models

TL;DR

This survey addresses the challenge of aligning large language models with human values while enhancing their reasoning capabilities by surveying reinforcement learning techniques tailored for LLMs. It covers foundational concepts, key algorithms (PPO, off-policy Q-learning, GRPO), and a broad portfolio of RL techniques (RLHF, RLAIF, DPO, constitutional AI, verifiable and stepwise reward methods) applied across instruction following, code generation, tool use, and domain-specific tasks. A comprehensive taxonomy and comparative analysis reveal trade-offs between reward modeling, feedback sources, and optimization strategies, highlighting trends toward offline and verifier-guided approaches and multi-objective alignment. The work provides a practical roadmap for researchers and practitioners aiming to balance capability, safety, and scalability in RL-driven LLM development, with emphasis on emerging directions like hybrid RL, hierarchical tool use, and multi-agent interactions.

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.