7 papers · 1 filter
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on c…
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
Mingyue Cheng, Yaguo Liu, Daoyu Wang +2
Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dy…
MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
Xiaoyu Tao, Mingyue Cheng, Ze Guo +4
Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)- based forecasters have made promising…
Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning
Daoyu Wang, Mingyue Cheng, Qingchuan Li +3
Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative appl…
SocraticPO: Policy Optimization via Interactive Guidance
Zirui Liu, Jie Ouyang, Qi Liu +8
Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewards provide an optimization dir…
Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting
Mingyue Cheng, Xiaoyu Tao, Qi Liu +2
Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While t…