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cs.LG2026
Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents
Zixuan Xie, Chang Xu, Yuhua Jiang +1
Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only after costly evaluations, and…
cs.LG2026
Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning
Zikang Shan, Han Zhong, Liwei Wang +1
Credit assignment is a central challenge in reinforcement learning (RL). Classical actor-critic methods address this challenge through fine-grained advantage estimation based on a…
cs.LG2026
OATS: Online Data Augmentation for Time Series Foundation Models
Junwei Deng, Chang Xu, Jiaqi W. Ma +5
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. E…