{
  "id": 10589385,
  "title": "When Can First-Order Models of Fine-Tuning Bound Forgetting?",
  "url": "https://urgent.news/2026/09/26/when-can-first-order-models-of-fine-tuning-bound-forgetting",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-26T17:45:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.32818v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Fine-tuning a language model on new data can make it forget facts that it should keep. We ask whether measurements taken at the start of a fine-tuning run can bound, for each protected fact, the probability that the run makes the model forget it. In LoRA fine-tuning with stochastic gradient descent on models from 0.6B to 14B parameters, a first-order response model estimated by finite-difference…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}