2 papers
cs.LG2026
One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models
Chris Cameron, Wangzheng Wang, Nikita Ivanov +3
Looped transformers scale computational depth without increasing parameter count by repeatedly applying a shared transformer block and can be used for iterative refinement, where e…
cs.LG2025
Leveraging Per-Instance Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik +5
We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearn…