Yuxin Ren

Learning Perturbation Robust Policies for LLM Agents with Stable Optimization

Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policies can be sensitive to various policy perturbations, such as hidden-state noise, pruning, and quantization. In this work, we study how to improve pertu...