Aug 2026· Computers and artificial intelligence· Vol 3, pp. 46-59· 0 citations· 26 references
TL;DR
This paper highlights the transition from static prediction to sequential decision-making, emphasizing RL’s strengths in long-term reward optimization and interaction modeling, and LLMs’ advantages in semantic understanding and reasoning.
Abstract
This paper examines the integration of large language models (LLMs) and reinforcement learning (RL) in recommender systems, focusing on their theoretical foundations and structural challenges. It highlights the transition from static prediction to sequential decision-making, emphasizing RL’s strengths in long-term reward optimization and interaction modeling, and LLMs’ advantages in semantic understanding and reasoning. Their complementary limitations—RL’s weak semantic representation and LLMs’ lack of long-term optimization—justify their integration. Existing research is classified into “LLM-enhanced RL” and “RL-shaped LLM,” with roles including representation enhancement, reward modeling, policy generation, and environment simulation, under varying coupling levels. The paper proposes a unified three-dimensional framework based on information sources, optimization time scale, and coupling strength, showing that performance differences arise from structural positioning rather than model scale. Key challenges include balancing expressiveness and efficiency, long-term optimization and training stability, and generalization versus specialization. The paper also identifies limitations in evaluation protocols and experimental design, calling for standardized frameworks for long-term value assessment. Overall, integrating LLMs and RL is crucial for advancing recommender systems toward intelligent decision-making agents, with future work focusing on stable coupling and unified evaluation.
A systematic literature review on how RL are adapted and scaled as a fundamental post-training tools and how innovations in the RL pipeline enhance the domain-specific LLMs is conducted.
Qianyue Hao, Lin Chen, Xiaoqian Qi et al.· ACM Computing Surveys· 1 citation
Recent advances at the intersection of reinforcement learning (RL) and Multimodal Foundation Models have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then organize more than 200 representative works into four thematic pillars: multi-modal large language models, visual generation, unified model frameworks, and vision-language-action models. For each pillar we examine algorithmic design, reward engineering, benchmark progress, and we distill trends such as curriculum-driven training, preference-aligned diffusion, and unified reward modeling. Finally, we review evaluation protocols spanning policy-level, trajectory-level preference, and training diagnostic stability, and we identify open challenges that include sample efficiency, generalization, and safe deployment. Our goal is to provide researchers and practitioners with a coherent map of the rapidly expanding landscape of visual RL and to highlight promising directions for future inquiry. Resources are available at: https://github.com/weijiawu/Awesome-RL-for-Multimodal-Foundation-Models.
The integration of Large Language Models (LLMs) with Reinforcement Learning (RL) for financial decision-making has grown rapidly in recent years, yet the literature remains fragmented and lacks systematic comparison across methods. In this survey we analyze 34 core studies (2023–2026), selected through a multi-stage process involving 84 initial candidates and 46 full-text reviews, and propose a three-paradigm taxonomy (feature-based, auxiliary-based, and policy-based) based on the functional role of LLMs within the RL pipeline. Analysis of these integration paradigms reveals an emergent architectural trade-off: while tighter policy-based coupling theoretically offers deeper contextual reasoning, it frequently introduces significant computational overhead and training instability. Conversely, simpler feature-based integration provides superior scalability and stability, though often at the expense of representational depth. Given the current benchmark fragmentation, the reported performance gains across these studies remain difficult to validate universally across different asset classes. Critical gaps identified include the insufficient handling of data leakage and look-ahead bias, standardized benchmarks, and limited alignment with regulatory frameworks such as MiFID II and the EU AI Act.
Ghusoon Hadi al-Aldaffaie, Alireza Taheri, Amirfarhad Farhadi et al.· Discover Artificial Intellig...· 0 citations
In the quantitative finance area, particularly in order execution, reinforcement learning (RL) has shown great promise due to its ability to interact with market environments based on real data. However, traditional RL methods suffer from slow research speed and rely on static market assumptions, which do not consider the impact of the agent's execution action on the environment. To address these, we propose a Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework. The framework utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code. SE-RL could continuously improve the accuracy of LLM-generated RL algorithms through a dual-enhancement kit at both high-level (prompt refinement) and low-level (parameter fine-tuning). Additionally, we use a multi-agent system to simulate dynamic financial markets, accounting for the impact of order executions on market dynamics. To further enhance training in such a dynamic market, we develop a hybrid environment training method that could rebalance each environment's loss weight. Comprehensive experiments on 200 realistic stock datasets demonstrate that our proposed framework outperforms current state-of-the-art baselines. Project page: https://kdd2026-se-rl.github.io/.
Vincent Fu, Xinxin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 0 citations
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Yi-Fan Yao, Bo Pang, Xuan-Phi Nguyen et al.· 0 citations
It is demonstrated that reward composition is a primary determinant of optimization outcomes, with effects as large as the decision to apply RL itself, and generalize to any structured generation task where quality is assessed along multiple automated dimensions.
Alexander Rombach, Chantale Lauer, Nijat Mehdiyev· 0 citations