Learnable Gain Parameters Enhance Model Performance
SeunghyunSEO7 · x · 2026-07-16
This post discusses a model architecture experiment. Inspired by Alec's architecture, the author added head gain, activation gain, and embedding residual/skip connection, observing a performance boost.
Although the main post is quite brief, its core message is clear: these learnable gain parameters and embedding skip connections could be highly effective structural modifications for improving model performance.
Related event: Researchers Debate 1/d-Style Attention Scaling in Modern LLMs(6 posts)→
More from Research
- Structural ensembles beat single predictions in TCR:pMHC generalization study — quaidmorris · 2026-07-22
- Structural ensembles, not single predictions, drive robust TCR:pMHC generalization — quaidmorris · 2026-07-22
- A 3D ray plot shows how hard this Jacobian counterexample is to read — moultano · 2026-07-22
- LLM leaderboards are now often measuring the harness too, Gary Marcus warns — GaryMarcus · 2026-07-22
- New paper defines self-state attacks, showing OS defenses leave four agent-memory cases indistinguishable — Justgototheeffinmoon · 2026-07-22
- Krea 2 users recommend a two-pass Clownshark sampler setup for sharper image details — listopalafoto · 2026-07-22