Reinforcement Learning–Guided Adaptive Tuning for Out-of-Distribution Harmful Text Detection
Year: 2026
Type: conference-paper
Abstract: As social media grows, harmful information spreads rapidly across platforms and evolves over time, showing cross-platform and crosstemporal variations.Existing methods rely on fixed model parameters during training, which fail to handle substantial semantic discrepancies, leading to Out-Of-Distribution (OOD) problems.While test-time tuning enables dynamic parameter adjustment, it may lead to excessive adaptation to individual samples.The key challenge is how to adapt to semantic variations during testing while preventing overfitting from continuous tuning.To tackle this issue, this paper proposes RLAT, a reinforcement learning (RL)-guided adaptive tuning method for harmful text detection.First, a tuning joint optimization module is designed to update parameters and adapt to semantic variations during testing.It tunes the model by optimizing consistency loss and applying word-level attention constraints to reduce over-reliance on local words and learn a more robust global representation.Then, to mitigate overfitting caused by continuous tuning, a RL-guided adaptive decision model is introduced to direct the tuning process.It reduces the influence of local samples by selecting data and controlling parameter updates, thereby improving overall test performance.Experimental results show that the RLAT outperforms state-of-the-art baselines in cross-platform and cross-temporal scenarios across multiple public datasets.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1966413184, G3666696391
OpenAlex ID: W7166824324