The proliferation of deceptive content in social networks necessitates robust Fake News Detection (FND) systems. Existing pipelines either train detectors on labeled data or leverage Large Language Models (LLMs) for their reasoning ability. However, current approaches remain either limited in generalizability or prone to over-commitment to persuasive yet flawed rationales, lacking systematic experience and mechanisms to expose subtle reasoning errors. We propose \textbf{RoE-FND} (\textbf{\underline{R}}eason \textbf{\underline{o}}n \textbf{\underline{E}}xperiences FND), an LLM-based framework that combines self-reflective experience building with deliberation through retrieved experiences for FND. RoE-FND builds an experience bank via reflective learning that compares an unconstrained analysis with a label-conditioned analysis using the ground-truth label as posterior supervision, then summarizes their critical divergence into reusable reasoning guidelines. During inference, RoE-FND generates two opposing deductions via a flipped pseudo-label provided as posterior, retrieves the most relevant experiences for resolving their key disagreement, and adjudicates the better-supported rationale as the final prediction. Experiments across five popular benchmarks, including text-only datasets, i.e., CHEF, Snopes, PolitiFact, and multimedia datasets, i.e., FakeTT, FakeSV, demonstrate that RoE-FND outperforms strong baselines without optimizing LLM parameters on dataset distributions, while exhibiting strong cross-dataset generalization.