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

In three lineseXTC combines structured prompt optimization and reinforcement learning for text classification. The system learns a natural language rulebook first, then distills reasoning from a teacher LLM into a compact model, then expands capabilities via RL. Result: fast inference with local reasoning traces and global modular explanations of learned domain rules.

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