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Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Yilin Li, Xunjian Yin, Yilin Chen, Xiaojun Wan
https://arxiv.org/abs/2508.18780 https://…

Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this field is still underexplored. Current research predominantly relies on supervised fine-tuning to train LLMs to directly generate the corrected sentence, which limits the model's powerful reasoning ability. To address this limitation, we propose a novel framework based on Rule-Based RL. Through experiments on the Chinese…

MAB Optimizer for Estimating Math Question Difficulty via Inverse CV without NLP
Surajit Das, Gourav Roy, Aleksei Eliseev, Ram Kumar Rajendran
https://arxiv.org/abs/2508.19014 h…

MAB Optimizer for Estimating Math Question Difficulty via Inverse CV without NLP
The evolution of technology and education is driving the emergence of Intelligent & Au- tonomous Tutoring Systems (IATS), where objective and domain-agnostic methods for determining question difficulty are essential. Traditional human labeling is subjective, and existing NLP-based ap- proaches fail in symbolic domains like algebra. This study introduces the Approach of Passive Measures among Educands (APME), a reinforcement learning-based Multi-Armed Bandit (MAB) framework that estimates diffic…

Breaking Barriers in Software Testing: The Power of AI-Driven Automation
Saba Naqvi, Mohammad Baqar
https://arxiv.org/abs/2508.16025 https://arxiv.org/pdf/…

Breaking Barriers in Software Testing: The Power of AI-Driven Automation
Software testing remains critical for ensuring reliability, yet traditional approaches are slow, costly, and prone to gaps in coverage. This paper presents an AI-driven framework that automates test case generation and validation using natural language processing (NLP), reinforcement learning (RL), and predictive models, embedded within a policy-driven trust and fairness model. The approach translates natural language requirements into executable tests, continuously optimizes them through learn…