reinforcement learning

Select-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance

Coordinated winter thermal management of fuel cell vehicles based on global-observation dual-agent reinforcement learning

Adaptive Biohybrid Neural Interface (ABNI): A Computational Feasibility Framework for Closed-Loop Neuromodulation, Thermal Safety, and System-Level Engineering Sub-Models

This paper presents the Adaptive Biohybrid Neural Interface (ABNI) as a computational feasibility framework: a set of independently verifiable mathematical and computational sub-models for the major subsystems such an interface would require, rather than an integrated, biologically validated device. Its core contributi...

Real-time reinforcement learning for turbulent state-dependent control in a bluff-body wake

Abstract Controlling turbulent dynamics remains a major challenge because their chaotic, multiscale nature strongly affects many fluid systems. Here, we present REACT, an autonomous reinforcement learning framework for real-time, state-dependent control of turbulent wake dynamics in a wind tunnel. Deployed on an Ahmed-...

Deep Diffusion Reinforcement Learning for Options Hedging

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. This work tests that premise using exact polynomial invariants recovered from trajectories and canonicalised as reduced Gröbner bases over ℚ. On Acrobot, exactness provides lit...

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. This work tests that premise using exact polynomial invariants recovered from trajectories and canonicalised as reduced Gröbner bases over ℚ. On Acrobot, exactness provides lit...

Fine-tuning small reasoning models for quantum field theory

Abstract Despite the growing application of large language models (LLMs) to theoretical physics, there has been little academic exploration of how domain-specific physics reasoning ability develops during training. To investigate this, we perform the first academic comparison of reinforcement learning and supervised fi...

A Survey on Reinforcement Learning Optimization Methods for Multi-Agent Collaboration of Large Language Models

The integration of reinforcement learning (RL) into the optimization of multi-agent collaboration for Large Language Models (LLMs) is an important combination of two advanced areas, Multi-Agent Systems (MAS) and LLMs. This paper thoroughly examines the main approaches, evaluation standards, recent progress and existing...

Artificial Intelligence in Quantitative Trading: Application Pipeline, Prospects, and Risk Governance

Artificial intelligence (AI) has become a significant force in the field of quantitative trading because it extends traditional rule-based systems to areas such as adaptive prediction, dynamic configuration and automated execution. At the same time, the widespread adoption of machine learning, reinforcement learning, a...

Advances in Reinforcement Learning for Retrieval-Augmented Generation in Large Language Model

The research findings show that RL has gradually expanded from simply improving the accuracy of the final answer to optimizing queries, multi-round search, process decision-making and trustworthy screening, providing new ideas for enhancing the active retrieval ability of RAG and improving the credibility of informatio...

Reinforcement Learning Methods and Optimization Strategies in Preference Alignment Techniques for Large Language Models

This paper investigates the development path of large language model preference alignment techniques, focusing on the training mechanism of Reinforcement Learning from Human Feedback and its three-stage process, including supervised fine-tuning, reward model training, and PPO-based policy optimization.