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Kaizen: Continuous model improvement via online learning
Jidoka: Drift detection stops bad predictions automatically
Just-in-Time: Retrain only when drift detected, not on schedule

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Online learning and dynamic retraining infrastructure Online Learning Infrastructure for Dynamic Model Retraining

This module provides incremental model updates without full retraining, supporting continuous improvement in production ML systems.

§References

corpus: Corpus Management for Online Learning
cpt: Continual Pre-Training (CPT) Pipeline (GH-448)
curriculum: Curriculum Learning for Progressive Training
dam: Differentiable Adaptive Merging (DAM) (GH-446)
distillation: Knowledge Distillation for Model Compression
distillation_advanced: Advanced Knowledge Distillation Strategies (GH-451)
dpo: Direct Preference Optimization (DPO) (GH-449)
drift: Drift Detection for Triggering Model Retraining
eval_harness: Evaluation Harness for Standard Benchmarks (GH-454)
moe_construction: Mixture of Experts (MoE) Construction from Dense Models (GH-445)
orchestrator: Retrain Orchestrator for Drift-Triggered Model Updates
per_layer_merge: Per-Layer Merge Granularity for Model Merging (GH-452)
rlvr: Reinforcement Learning on Verifiable Rewards (RLVR) (GH-450)
tokenizer_surgery: Tokenizer Surgery for Vocabulary Transplantation (GH-447)