src_fb50be84b835134c75
- Source-provided label
- Not reported
- Evidence class
- mentioned in report
- Confidence
- Not reported
- Observed
- Oct 07, 2026 04:00 UTC
- Retrieved
- Oct 08, 2026 03:43 UTC
ASCENT: First-Order Optimal Fine-Tuning with Recalibration for Safety--Utility Co-Enhancement
arXiv:2610.08061v1 Announce Type: new Abstract: Supervised fine-tuning can substantially improve the downstream utility of large language models (LLMs) but may compromise their safety. Existing safety-preserving methods constrain downstream updates using safety-related parameters or subspaces, but mainly focus on safety preservation rather than joint safety and utility enhancement, lack a theoreti
Reference details
- Source category
- Research & news
- Report title
ASCENT: First-Order Optimal Fine-Tuning with Recalibration for Safety--Utility Co-Enhancement- First reported
- Oct 07, 2026 04:00 UTC
- Last reported
- Oct 07, 2026 04:00 UTC
- Catalog updated
- Oct 08, 2026 03:43 UTC
- Record ID
39645940b92662f8875326a14a3b95597e72ad022e7d392501e660532557792e
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Source observations
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Review source update dates and status
- src_fb50be84b835134c75Collection status unverifiedLast successful batch: Unverified
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