TaxonRL: Reinforcement Learning with Intermediate Rewards for Interpretable Fine-Grained Visual Reasoning
Traditional vision-language models struggle with contrastive fine-grained taxonomic reasoning, particularly when distinguishing between visually similar species within the same genus or family.
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Key Takeaways
- arXiv cs.CL introduces TaxonRL, a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decomposes the reasoning process into hierarchical…
- arXiv cs.CL's 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.
What It Means
Context
arXiv cs.CL introduces TaxonRL, a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decomposes the reasoning process into hierarchical taxonomic predictions. arXiv cs.CL's 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. arXiv cs.CL demonstrates strong cross-domain generalization, showing substantial gains in primate and marine species verification. arXiv cs.CL's results establish that enforcing structured, hierarchical reasoning provides a powerful and transferable framework for fine-grained visual discrimination.
For builders
arXiv cs.CL introduces TaxonRL, a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decomposes the reasoning process into hierarchical…
For Builders
arXiv cs.CL introduces TaxonRL, a reinforcement learning approach using Group Relative Policy Optimization with intermediate rewards that decomposes the reasoning process into hierarchical…