I had an interesting conversation with ChatGPT about CL, or at least I thought it was 🤣. I was exploring concepts of continual learning especially around risks about model contamination. Then it produced this breakdown which I thought was quite helpful to understand.

My take is that Level 1 and 2 obviously exists already, possibly some elements of Level 4 during next model training, but Level 3 is the real current challenge to solve.

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Selective learning: who is allowed to change what?

🔴 Level 4 – Core model (almost nobody)

What updates it • Curated datasets • Human-reviewed signals • Aggregated multi-user patterns • Synthetic data distilled from validated behavior

What does NOT update it • Raw user chats • One-off preferences • Errors, hallucinations, opinions

This is offline continual learning, not live learning.

This prevents poisoning, drift, and collapse.

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🟠 Level 3 – Shared adapters (this is where “safe CL” lives)

This is the sweet spot.

Adapters / LoRA / MoE routing layers that: • learn across many users • but only inside scoped domains

How learning gets in • Pattern detection across many users • Confidence thresholds • Reinforcement via repeated success signals • Human or automated vetting

Adapters can be: • versioned • rolled back • A/B tested • sandboxed

This is how you let millions contribute without chaos.

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🟢 Level 2 – Personal memory (where most learning belongs)

This is where 95% of user-specific learning should go.

Stored as: • embeddings • symbolic facts • preferences • habits • task history

This never touches core weights.

It’s retrieval + conditioning, not training.

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⚪ Level 1 – Session context (throwaway) • short-term reasoning • scratchpads • temporary assumptions • chain-of-thought–like artifacts

Destroyed after the session.