AsyPO: Asymmetric Post-Training for GUI Agents with Segment-Level Reasoning Guidance

Graphical user interface (GUI) agents require both executable actions and reasoning, but existing outcome-based post-training provides only coarse supervision for the latter. Existing post-training methods mainly include reinforcement learning (RL) and on-policy self-distillation (OPSD). Although OPSD enables process-level supervision through teacher–student token probabilities, applying it independently to long reasoning spans can distort their global structure. We propose an asymmetric post-training framework that combines action-level Group Relative Policy Optimization with relative segment-level reasoning guidance. Our method aggregates teacher guidance within reasoning segments, centers the resulting signals across each reasoning span, and selectively applies them through negative-advantage gating. This provides targeted reasoning supervision while preserving the overall structure of model-generated reasoning. Experiments on OSWorld and OmniACT demonstrate consistent improvements over the evaluated baselines at both 4B and 8B model scales.