Learning Perturbation Robust Policies for LLM Agents with Stable Optimization.

arXiv:2609.34064v1 Announce Type: cross Abstract: 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 per