What question did this study set out to answer?

The research aims to enhance sentence simplification through optimized prompts using reinforcement learning techniques.

February 16, 2026

RL Based Adaptive Prompt Optimization for User-Centric Structured Sentence Simplification via Small Language Models

Key Points

The research aims to enhance sentence simplification through optimized prompts using reinforcement learning techniques.
Developed an RL-based framework using a lightweight PPO policy.
Utilized a frozen small-scale LLaMA-3.2B model for sentence simplification.
Compared RL-optimized prompts against manual baselines.
RL-optimized prompts outperformed manual baselines in semantic fidelity and logical coherence.
Smaller LLaMA-3.2B achieved comparable results to a larger LLaMA-3.3 70B model in clarity and instructional value.

Abstract

We introduce a reinforcement learning (RL)-based framework to optimize discrete natural language prompts for enhancing both the accuracy and clarity in sentence simplification. Using a lightweight PPO policy, our method learns to guide a frozen small-scale LLaMA-3.2B model toward effective simplification for supporting user-centric computational thinking tasks. Results show that our RL-optimized prompts significantly surpass manual baselines in semantic fidelity, logical coherence, and instructional quality. Moreover, the proposed RL-optimized prompting approach enables a much smaller LLM to achieve results that are comparable in clarity and instructional value to those produced by a much larger LLaMA-3.3 70B model.

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