Selective Forgetting for Large Reasoning Models

📰 ArXiv cs.AI

Selective forgetting for Large Reasoning Models (LRMs) addresses knowledge leakage and memorization of sensitive information

advanced Published 7 Apr 2026
Action Steps
  1. Identify sensitive information in training data
  2. Implement selective forgetting techniques to remove or forget sensitive information
  3. Evaluate model performance after applying selective forgetting
  4. Refine and adjust selective forgetting methods as needed
Who Needs to Know This

AI engineers and researchers working on large reasoning models can benefit from this concept to ensure ethical and legal compliance, while data scientists and ML researchers can apply these techniques to improve model reliability

Key Insight

💡 Selective forgetting can help mitigate ethical and legal concerns associated with LRMs

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💡 Selective forgetting for Large Reasoning Models (LRMs) tackles knowledge leakage and sensitive info memorization

Key Takeaways

Selective forgetting for Large Reasoning Models (LRMs) addresses knowledge leakage and memorization of sensitive information

Full Article

Title: Selective Forgetting for Large Reasoning Models

Abstract:
arXiv:2604.03571v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) generate structured chains of thought (CoTs) before producing final answers, making them especially vulnerable to knowledge leakage through intermediate reasoning steps. Yet, the memorization of sensitive information in the training data such as copyrighted and private content has led to ethical and legal concerns. To address these issues, selective forgetting (also known as machine unlearning) has emerged as a potenti
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