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What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations — arXiv:2610.41616

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41616
License (as archived): CC0-1.0
Archived: 2026-11-02T07:09:25.989Z
SHA-256 of the archived text: 923679bebbddee7f0a69ffe749b7da8966cacf81c143018584a874f3ff78ce98
Stable URL of this page: https://metraipolis.com/documento/dc_7c7eb14f4e76

How to cite

What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations — arXiv:2610.41616 — arXiv (en), https://arxiv.org/abs/2610.41616 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7c7eb14f4e76 (Metraipolis archive dc_7c7eb14f4e76, sha256 923679bebbddee7f0a69ffe749b7da8966cacf81c143018584a874f3ff78ce98)

BibTeX

@misc{dc7c7eb14f4e76,
  title = {What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations — arXiv:2610.41616},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41616},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7c7eb14f4e76; sha256 923679bebbddee7f0a69ffe749b7da8966cacf81c143018584a874f3ff78ce98},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41616. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) acquire extensive factual knowledge during pre-training, which may include sensitive, private, or copyrighted information, motivating the development of unlearning methods that selectively remove specific knowledge while preserving general capabilities. However, existing approaches typically optimize entire responses without explicitly identifying the knowledge to be forgotten, resulting in coarse and uncontrolled forgetting. To address this, we revisit the forgetting target of LLM unlearning and introduce Knowledge Association (KA), which characterizes the association between a query context and its target knowledge and is instantiated by the corresponding conditional predictive distribution, as the fundamental target of forgetting. Based on KA, we propose Knowledge Association Reinforcement Unlearning (KARU), a unified reinforcement learning framework that suppresses forgotten knowledge associations while preserving retained ones. Specifically, KARU derives complementary mechanisms from the KA-based formulation: token-level reward construction for finer-grained forgetting supervision, distribution-level regularization for precise knowledge suppression, and proximity-aware retain sampling to mitigate collateral degradation. Experiments across diverse LLMs on representative benchmarks demonstrate that KARU consistently improves forgetting effectiveness over existing baselines while maintaining comparable retention performance and general utility.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41616 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:06:59.348Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).