frontier models
Fine-Tuning Protects 99 Percent of the Weight Space
Subspace Tuning confines updates to lower singular directions to preserve prior capabilities.
Summary
Subspace Tuning confines updates to lower singular directions to preserve prior capabilities.
The authors report that protecting as much as 99 percent of the weight subspace improves retention while maintaining target-task performance, with two to four times less distribution drift than baseline recipes. The results advance a benchmark tradeoff; they do not eliminate forgetting in every model or domain.
Why it matters
Subspace Tuning confines updates to lower singular directions to preserve prior capabilities.
Limits and context
- The results advance a benchmark tradeoff; they do not eliminate forgetting in every model or domain.
Key claims
Subspace Tuning confines updates to lower singular directions to preserve prior capabilities.
Qualification: The results advance a benchmark tradeoff; they do not eliminate forgetting in every model or domain.
Evidence: source-2026-10-16-019
Sources
- arXiv preprint 2610.22293, original v1arXiv / submitting research authors · primary research
Corrections
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