executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
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mas_regularized_training
mas_regularized_trainingApply the MAS quadratic regularizer during later-task training and log parameter drift evidence.
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GitHubname: 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.