MIT Unveils Weight-Space Programming, Tiny Weight Edits Boost Models Without Training
MIT CSAIL researchers introduced Weight-Space Programming, letting GPT-6 Astra directly edit Qwen's weights. Modifying just 0.0002% of weights without training boosted math performance 6x, surpassing the original model.
2026-11-05 ~ 2026-11-05 · 2 related posts
- Episode 1: MIT paper: GPT-6 edits 0.016% of weights, boosting small model math 6x without training(2026-11-03, 159 posts)
- Episode 2: Editing 0.016% of weights fixes Qwen reasoning loops, lifting MATH-500 to 48.1%(2026-11-03, 2 posts)
- Episode 3: Old hands-on guide resurfaces as MIT weight-editing claims spark interest(2026-11-03, 2 posts)
- Episode 4: LLM-fixed driving nets still work with weights zeroed out(2026-11-04, 2 posts)
- Episode 5: Audit of MIT Weight-Space Programming paper: results hold, safety concerns remain(2026-11-04, 2 posts)
- Episode 6: MIT Unveils Weight-Space Programming, Tiny Weight Edits Boost Models Without Training(2026-11-05, 2 posts)
- MIT's Weight-Space Programming: LLMs edit weights natively, fixing Qwen math with 0.016% of weights — dbreunig · 2026-11-05
- MIT shows GPT-6 Astra editing Qwen weights directly, boosting MATH-500 from 7.9% to 48.1% — lateinteraction · 2026-11-05