## 1 Introduction  
Machine learning models rely heavily on high-quality data, yet real-world datasets often suffer from corrupted data--such as missing entries or noisy measurements--which significantly degrade model performance. Data corruption arises from diverse sources, including sensor errors, transmission artifacts, or incomplete data collection. It manifests in two primary forms: missing data (e.g., masked tokens in NLP or undetected vehicles in traffic control) and noisy data (e.g., mislabeled text or perturbed sensor readings), while this challenge is well documented in classical machine learning ([PERSON] and [PERSON], 2019 [1]; [PERSON] et al., 2021 [2]), its implications for modern paradigms like large language models (LLMs) and deep reinforcement learning (DRL) remain underexplored.  
Different learning paradigms exhibit distinct vulnerabilities to data corruption. For example, LLMs may tolerate certain missing tokens but struggle with semantic noise, whereas DRL policies can compound observational noise over sequential decisions. Prior work has shown that noise in NLP tasks (e.g., mislabeled text) biases model outputs ([PERSON] et al., 2020 [3]), whereas in DRL, observational noise disrupts policy stability ([PERSON] et al., 2017 [4]). Despite these insights, fundamental questions remain unanswered: How do corruption type and severity impact different learning paradigms? Can imputation effectively recover lost performance, or does it can make things worse by introducing new noise? And when is collecting more data a viable alternative to cleaning existing data? While traditional imputation methods--including statistical interpolation and deep generative approaches--offer partial solutions, their efficacy varies significantly across tasks. This variation highlights persistent gaps in our understanding of the trade-offs between data quality, quantity, and imputation strategies in machine learning systems.  
This study bridges these gaps through a systematic analysis of two distinct machine learning paradigms: supervised NLP tasks (NLP-SL) and deep reinforcement learning for traffic signal control (Signal-RL). It evaluates how varying corruption levels affect model performances, quantify the trade-offs of imputation strategies, and assess whether expanding datasets can counteract corruption effects. The experiments reveal universal patterns (such as diminishing returns from data quality improvements), paradigm-specific insights (such as DRL's heightened sensitivity to noise), as well as task-specific observations (such as the critical 30% of data that determines performance in traffic signal control). This work provides actionable guidelines for designing robust systems in noisy environments, emphasizing smart data prioritization, imputation decisions, and noise-aware preprocessing. Key innovations include:  
* **Modeling Performance Degradation**: This study shows that performance degradation follows a diminishing-return pattern, well-captured by an exponential function, and reveal task-specific sensitivities.
* **Imputation Trade-offs**: This study demonstrates the boundary conditions where imputation is beneficial versus harmful, providing actionable guidelines for practitioners.
* **Data Quantity-Quality Trade-offs**: This study empirically shows that larger datasets can only partially offset corruption effects, with diminishing utility at high corruption levels.  
By addressing these questions, our study advances the understanding of data corruption's impact on machine learning and provides a framework for future research in robust model development. The code for reproducing our experiments is available at [[https://github.com/qiliuchn/data-corruption-study](https://github.com/qiliuchn/data-corruption-study)]([https://github.com/qiliuchn/data-corruption-study](https://github.com/qiliuchn/data-corruption-study)) (accessed on 20 May 2025).