用中间奖励训练模型分步推理物种分类，准确率超人类且过程可解释。
TaxonRL: Reinforcement Learning with Intermediate Rewards for Interpretable Fine-Grained Visual Reasoning
- 分层级预测物种、属、科特征，中间奖励引导逐步推理
- 在Birds-to-Words上达91.7%准确率，超过人类77.3%
- 结果可追溯，适合需要透明决策的生物分类场景
传统视觉-语言模型在区分同属或同科内视觉相似物种时表现不佳。我们提出TaxonRL，一种基于组相对策略优化与中间奖励的强化学习方法，将推理过程分解为层级化的分类判断。该方法激励模型在最终分类前显式分析物种级、属级和科级特征，不仅提升准确率，还生成可验证的决策路径。在Birds-to-Words数据集上，TaxonRL平均准确率达91.7%，超过人类表现（77.3%），并生成可解释的推理轨迹。我们进一步验证了其在灵长类与海洋物种识别中的强跨域泛化能力。结果表明，结构化层级推理是细粒度视觉区分的有效且可迁移的框架。
原文摘要 · Abstract (English)
Traditional vision-language models struggle with contrastive fine-grained taxonomic reasoning, particularly when distinguishing between visually similar species within the same genus or family. We introduce TaxonRL, a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decomposes the reasoning process into hierarchical taxonomic predictions. Our method incentivizes models to explicitly reason about species-level, genus-level, and family-level features before making final classifications. This structured approach is designed not only to boost accuracy but also to yield a transparent, verifiable decision-making process. On the challenging Birds-to-Words dataset, TaxonRL achieves 91.7\% average accuracy, exceeding human performance (77.3\%) while generating interpretable reasoning traces. We demonstrate strong cross-domain generalization, showing substantial gains in primate and marine species verification. Our results establish that enforcing structured, hierarchical reasoning provides a powerful and transferable framework for fine-grained visual discrimination.
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