AI RESEARCH
Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions
arXiv CS.LG
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ArXi:2606.03382v1 Announce Type: new While Proximal Policy Optimization (PPO) nstrates strong performance in stationary settings, we show that its standard optimization paradigm struggles in continual and non-stationary environments. The failure does not stem from insufficient model capacity or overly restrictive clipping. Instead, PPO performs persistent, directionally inefficient local updates, which indicates a lack of geometry-aware guidance for accumulating meaningful behavioral change and ultimately hindering transitions toward new behavior patterns.