3 accepted papers
Current large language model (LLM) applications often employ multi-component prompts, comprising both system and user prompts, to guide model behaviors. While recent advancements have demonstrated the efficacy of automatically optimizing either the system or user prompt to boost performance, such un…
Mean-field theory offers a promising solution to the scalability issues encountered in multi-agent reinforcement learning (MARL) within large-scale systems. However, most existing MARL algorithms based on mean-field theory are typically constrained to discrete action space. In continuous action spac…
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, Reinforcement Learning (RL) — which operates on a reward-centric mechanism for optimal control — has surfaced as…