{
  "id": 5677,
  "url": "https://arxiv.org/abs/2604.17207v1",
  "title": "Demystifying the unreasonable effectiveness of online alignment methods",
  "summary": "Iterative alignment methods based on purely greedy updates are remarkably effective in practice, yet existing theoretical guarantees of \\(O(\\log T)\\) KL-regularized regret can seem pessimistic relative to their empirical performance. In this paper, we argue that this mismatch arises from the regret criterion itself: KL-regularized regret conflates the statistical cost of learning with the exploratory randomization induced by the softened training policy. To separate these effects, we study the t",
  "authors": "Enoch Hyunwook Kang",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-19T02:20:36.000Z",
  "fetched_at": "2026-07-14T16:31:57.533Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5677",
  "original_url": "https://arxiv.org/abs/2604.17207v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}