Initial Guesses Matter for NLA
Turn_Trout · x · 2026-07-13
Natural Language Autoencoders (NLA) are fascinating: they take residual stream vectors and use natural language to explain what the model is "thinking."
This thread points out that training doesn't start from ground truth. Instead, Claude makes a "warm start" guess about the representations, followed by fine-tuning the encoder and decoder. The author's finding is that these initial guesses aren't just noise; they significantly impact the final training results.
More from Research
- Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11
- VidMap uses RoMa coarse matching on all frames, fine-scale only for keyframes — ducha_aiki · 2026-09-11
- Bug Hunt Bench author: leaderboard noise is about 2-3 points — PawelHuryn · 2026-09-11
- PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
- New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11
- LoMa Paper Ships REALLY HardPairs Dataset, Accepted at ECCV 2026 — ducha_aiki · 2026-09-11