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This survey paper examines the intersection of deep reinforcement learning (DRL) and foundation models (FMs), focusing on techniques like RLHF, RLAIF, and world-model pretraining for refining FM capabilities. It categorizes existing research into model-centric, RL-centric, and hybrid integration pathways, highlighting applications in language, multimodal agents, and scientific discovery. The paper identifies challenges hindering DRL-FM integration and suggests future research directions for reinforcement-driven adaptation of intelligent systems.
DRL's convergence with foundation models promises powerful AI systems, but faces critical challenges in scalability, reliability, and ethical alignment.
Deep reinforcement learning (DRL) and large foundation models (FMs) have reshaped modern artificial intelligence (AI) by enabling systems that learn from interaction while leveraging broad generalization and multimodal reasoning capabilities. This survey examines the growing convergence of these paradigms and reviews how reinforcement learning from human feedback (RLHF), reinforcement learning from AI feedback (RLAIF), world-model pretraining, and preference-based optimization refine foundation model capabilities. We organize existing work into a taxonomy of model-centric, RL-centric, and hybrid DRL–FM integration pathways, and synthesize applications across language and multimodal agents, autonomous control, scientific discovery, and societal and ethical alignment. We also identify technical, behavioral, and governance challenges that hinder scalable and reliable DRL–FM integration, and outline emerging research directions that suggest how reinforcement-driven adaptation may shape the next generation of intelligent systems. This review provides researchers and practitioners with a structured overview of the current state and future trajectory of DRL in the era of foundation models.
