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

2026-05-27 20:29:41
Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang, Leman Akoglu
arXiv_AI
               
Abstract

LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability. We introduce eXTC (eXplainable Text Classifier) with three progressive stages: (1) learning a Standard Operating Procedure (SOP, or rulebook) in natural language via a new Structured Prompt Optimization algorithm; (2) SOP-grounded reasoning distillation from a large teacher LLM into a compact LM; and (3) expanding reasoning capabilities beyond the initial SOP via reinforcement learning. This design enables eXTC to provide (i) fast inference via a compact LM, with (ii) inference-time local reasoning traces, alongside a global, modular explanation of its learned domain rules, while (iii) significantly outperforming existing paradigms across diverse benchmarks in both classification performance and explanation quality, with stage-by-stage gains.

Abstract (translated)

URL

https://arxiv.org/abs/2605.29076

PDF

https://arxiv.org/pdf/2605.29076.pdf

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