1 citations · 1 across the 6 of their papers we have counts for
6 papers
Progress Reward Model for Reinforcement Learning via Large Language Models
Xiuhui Zhang, Ning Gao, Xingyu Jiang +4
Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large language m…
SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents
Xiwen Chen, Wenhui Zhu, Songzhu Zheng +3
Large language models (LLMs) are increasingly deployed for autonomous financial trading, a domain requiring continuous adaptation to noisy, non-stationary markets. Existing self-im…
SODA: Semi On-Policy Black-Box Distillation for Large Language Models
Xiwen Chen, Jingjing Wang, Wenhui Zhu +7
Black-box knowledge distillation for large language models presents a strict trade-off. Simple off-policy methods (e.g., sequence-level knowledge distillation) struggle to correct…
RF-Agent: Automated Reward Function Design via Language Agent Tree Search
Ning Gao, Xiuhui Zhang, Xingyu Jiang +3
Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Mode…
EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Yang Cheng, Weiyu Ma, Zilai Wang +6
Complex decision-making often requires agents to progress through intermediate tasks rather than solve the final target directly. Existing LLM self-refinement methods typically ite…
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement
Junjie Lin, Jian Zhao, Lin Liu +6
Traditionally, AI development for two-player zero-sum games has relied on two primary techniques: decision trees and reinforcement learning (RL). A common approach involves using a…