title: Antidistillation Sampling
summary: Frontier models that generate extended reasoning traces inadvertently produce
  rich token sequences that can facilitate model distillation. Recognizing this
  vulnerability, model owners may seek sampling strategies that limit the
  effectiveness of distillation without compromising model performance.
  Antidistillation sampling provides exactly this capability. By
  strategically modifying a model's next-token probability distribution,
  antidistillation sampling poisons reasoning traces, rendering them
  significantly less effective for distillation while preserving the model's
  practical utility. For further details, see https://antidistillation.com.
paper_authors._id: 6801b77dcb758561ae26997e
paper_authors.name: Yash Savani
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:39:03.683Z
paper_authors._id: 647c0564001553a39c38e79e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/t5KF0Vp7kCk-5EZRWhG8i.jpeg
paper_authors.fullname: Yash Savani
paper_authors.type: user
paper_authors.user: yashsavani
paper_authors._id: 6801b77dcb758561ae26997f
paper_authors.name: Asher Trockman
paper_authors._id: 6801b77dcb758561ae269980
paper_authors.name: Zhili Feng
paper_authors._id: 6801b77dcb758561ae269981
paper_authors.name: Avi Schwarzschild
paper_authors._id: 6801b77dcb758561ae269982
paper_authors.name: Alexander Robey
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:39:01.839Z
paper_authors._id: 63458a32d54fb141deda949d
paper_authors.avatarUrl: /avatars/fc4b59cd009075ac7987c6cdddbe3fea.svg
paper_authors.fullname: Alex Robey
paper_authors.type: user
paper_authors.user: arobey1
paper_authors._id: 6801b77dcb758561ae269983
paper_authors.name: Marc Finzi
paper_authors._id: 6801b77dcb758561ae269984
paper_authors.name: J. Zico Kolter
paper_title: Antidistillation Sampling
paper_summary: Frontier models that generate extended reasoning traces inadvertently produce
  rich token sequences that can facilitate model distillation. Recognizing this
  vulnerability, model owners may seek sampling strategies that limit the
  effectiveness of distillation without compromising model performance.
  Antidistillation sampling provides exactly this capability. By
  strategically modifying a model's next-token probability distribution,
  antidistillation sampling poisons reasoning traces, rendering them
  significantly less effective for distillation while preserving the model's
  practical utility. For further details, see https://antidistillation.com.
paper_ai_summary: Antidistillation sampling modifies a model's next-token probability distribution to disrupt the generation of reasoning traces for distillation without affecting model performance.
paper_ai_keywords: antidistillation sampling next-token probability distribution reasoning traces model distillation