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

• URL: http://arxiv.org/abs/2605.29076v2
• Date: Tue, 02 Jun 2026 23:33:43 GMT
• Title: Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
• Abstract summary: LLMs have advanced text classification, yet existing paradigms face a trade-off.<n>We introduce eXTC with three progressive stages: learning a SOP, reasoning distillation from a large teacher LLM into a compact LM, and expanding reasoning capabilities beyond the initial SOP.<n>This design enables (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.
• Score: 14.95562040863983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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