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Mitigating Reasoning-Induced Misalignment via Safety-Direction Penalty

Research LLMs & Foundation Models

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TL;DR - This paper analyzes how harmless reasoning fine-tuning can sometimes degrade LLM safety and introduces a Safety-Direction Penalty (SDP) to mitigate that effect. On Qwen2.5-3B and 7B, SDP restores safety while preserving benchmark reasoning performance.

  • Identifies coupled activation-space directions associated with reasoning ability and safety behavior.
  • Finds that larger safety-representation shifts correlate with greater safety degradation.
  • Uses CKA distance ratios and probes to locate layers most relevant to safety decisions.
  • Penalizes movement along the learned safety direction during fine-tuning, expanding the targeted layers when diagnostics reveal compensatory shifts elsewhere.

Sources (1)

Mitigating Reasoning-Induced Misalignment via Safety-Direction Penalty

arXiv cs.AI Yipeng Zhao, Qishun Yang, Shenzhe Zhu, Shu Yang, Di Wang 2026-08-24 arXiv:2608.23497
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-21 14:32:11.477448 UTC

TL;DR - This paper analyzes how harmless reasoning fine-tuning can sometimes degrade LLM safety and introduces a Safety-Direction Penalty (SDP) to mitigate that effect. On Qwen2.5-3B and 7B, SDP restores safety while preserving benchmark reasoning performance.

  • Identifies coupled activation-space directions associated with reasoning ability and safety behavior.
  • Finds that larger safety-representation shifts correlate with greater safety degradation.
  • Uses CKA distance ratios and probes to locate layers most relevant to safety decisions.
  • Penalizes movement along the learned safety direction during fine-tuning, expanding the targeted layers when diagnostics reveal compensatory shifts elsewhere.
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