做环境监测或灾害预测的团队，EAPO 解决了分布漂移下罕见事件难检测的痛点，值得在类似长尾预测任务中试试。
野火预测面临罕见极端事件和分布漂移的双重挑战，标准模型常因忽视少数类（火灾）而失效。研究者提出环境自适应偏好优化（EAPO）框架，通过k近邻检索构建与目标环境对齐的数据集，再结合监督学习和偏好优化进行混合微调，重点强化对罕见极端事件的识别。在真实野火预测任务中，EAPO在环境变化下达到ROC-AUC 0.7310，显著提升了极端事件的检测能力。该工作为动态环境下的罕见事件预测提供了新思路。
Environment-Adaptive Preference Optimization for Wildfire Prediction
Predicting rare extreme events such as wildfires from meteorological data requires models that remain reliable under evolving environmental conditions. This problem is inherently long-tailed: wildfire events are rare but high-impact, while most observations correspond to non-fire conditions, causing standard learning objectives to underemphasize the minority class (fire) that matters most. In addition, models trained on historical distributions often fail under distribution shifts, exhibiting degraded performance in new environments. To this end, we propose Environment-Adaptive Preference Optimization (EAPO), a framework that adapts prediction to the target environment with long-tail distribution. Given a new input distribution, we first construct distribution-aligned datasets via $k$-nearest neighbor retrieval. We then perform a hybrid fine-tuning procedure on this local manifold, combining supervised learning with preference optimization, as well as emphasizing on rare extreme events. EAPO refines decision boundaries while avoiding conflicting signals from heterogeneous training data. We evaluate EAPO on a real-world wildfire prediction task with environmental shifts. EAPO achieves robust performance (ROC-AUC 0.7310) and improves detection in extreme regimes, demonstrating its effectiveness in dynamic wildfire prediction systems.