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cs.LG2026
Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning
Shijin Gong, Kai Ye, Jin Zhu +3
Recent advances in large language models (LLMs) have increasingly relied on reinforcement learning (RL) to improve their reasoning capabilities. Three types of approaches have been…
cs.LG2026
What Do World Models Learn in RL? Probing Latent Representations in Learned Environment Simulators
Xinyu Zhang
World models learn to simulate environment dynamics from experience, enabling sample-efficient reinforcement learning. But what do these models actually represent internally? We ap…
cs.LG2024
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li +4
Developing policies that can adjust to non-stationary environments is essential for real-world reinforcement learning applications. However, learning such adaptable policies in off…