Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text

Researchers introduce eXTC, a new framework combining structured prompt optimization with reinforcement learning to create interpretable text classifiers that balance performance with explainability. The system generates human-readable domain rules while maintaining inference speed through knowledge distillation, addressing a longstanding trade-off in AI transparency.

The research addresses a fundamental challenge in large language model deployment: achieving both high performance and interpretability in text classification tasks. Traditional approaches force practitioners to choose between supervised fine-tuning, which scales well but provides little insight into model reasoning, and discrete prompt optimization, which offers transparency but struggles with performance and computational efficiency. eXTC's three-stage architecture resolves this tension by first extracting domain knowledge as natural language rules through structured prompt optimization, then distilling this reasoning into a compact model for fast inference, and finally extending capabilities through reinforcement learning.

This work reflects broader industry concerns about AI transparency and accountability. As language models increasingly influence critical decisions in healthcare, finance, and legal domains, stakeholders demand not just accurate predictions but understandable reasoning. The ability to generate both local explanations (per-instance reasoning traces) and global explanations (learned domain rules) addresses regulatory requirements and user trust.

For AI practitioners and enterprises, eXTC demonstrates that interpretability need not come at significant performance cost. The framework's modular design enables domain experts to verify learned rules and identify potential biases before deployment. For researchers, the combination of prompt optimization with reinforcement learning establishes a new paradigm for building explainable systems at scale.

The practical implications extend to industries where model decisions require justification to regulators or end-users. Future work should focus on scaling eXTC to larger datasets and more complex reasoning tasks, as well as validating whether extracted rules genuinely reflect model behavior or merely approximate it.

• →eXTC resolves the interpretability-performance trade-off by combining structured prompt optimization with knowledge distillation and reinforcement learning.
• →The framework generates both local inference-time explanations and global domain rules in natural language for human verification.
• →Compact model size enables fast inference while maintaining reasoning transparency, critical for regulated industries.
• →Multi-stage architecture allows progressive capability expansion beyond initial rule-based reasoning through reinforcement learning.
• →Outperforms existing paradigms on classification performance and explanation quality across diverse benchmarks.