Apply the MAS quadratic regularizer during later-task training and log parameter drift evidence.
Scanned 9/9/2026
Install to Claude Code
Skills Directorynpx -y skills add VectorSpaceLab/AREX-Skill --skill mas_regularized_training --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Mas Regularized Training?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-mas-regularized-training)More formats (shields.io, HTML) on the badges page.
---
name: mas_regularized_training
description: Apply the MAS quadratic regularizer during later-task training and log parameter drift evidence.
---
# MAS Regularized Training
Use for later-task optimizer steps with `task_loss + lambda * sum(Omega * (theta-theta_star)^2)`. Inputs are weights, task data, snapshot, importance, lambda, and optimizer settings. Outputs are updated parameters and loss/penalty traces. Validate with `python tests/test_regularizer.py`.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
