RSI-Nav: Recursive Self-Improvement with Proactive Preflection for Training-Free Object-Goal Navigation

Training-Free Object-Goal Navigation (TF-OGN) requires embodied agents to explore and locate target objects in unseen environments without any task-specific training. To this end, recent methods leverage foundation models. \bchange{However, many of the compared TF-OGN methods do not explicitly maintain memory conditioned on outcomes across episodes, limiting systematic reuse of completed experience. In this paper, we propose RSI-Nav, a TF-OGN framework that \rsiupdate{performs recursive self-improvement (RSI) by using completed navigation outcomes to update the rule memory guiding future decisions. Specifically, we build an agentic rule memory by extracting actionable knowledge from past trajectories. We retrieve rules by combining trajectory-derived semantic credit and historical success through UCB-based utility and exploration. In addition, we introduce a memory-guided preflection module that forecasts potential outcomes before action, reducing inefficient exploration. Extensive experiments show that our method outperforms existing training-free baselines, achieving a 10.1% relative improvement in success rate on MP3D.