Or: When Memories Get Good -- The Default Path Without Theoretical Breakthroughs

Epistemic status: Fairly confident in the core thesis (context + memory can substitute for weight updates for most practical purposes). The RL training loop is a sketch, not a tested proposal. I haven't done a thorough literature review.

Suppose there are no major breakthroughs in continual learning -- that is, suppose we continue to struggle at using information gathered at runtime to update the weights of a given instance of an AI model. If you try to update the weights at runtime today, usually you end up with catastrophic forgetting, or you find you can only make very small updates with the tiny amount of useful data you have [1] .

So, if you can’t train a day's worth of information into the model, how could you end up with something that functions as if it were learning on the job?

Long Context Lengths, High Quality Summaries, and Detailed Documentation [2] [3] .

It's a straightforward idea, and basically done today, just not particularly well yet. Laying it out:

The model does some task. In doing so, it gathers a [...]

The original text contained 16 footnotes which were omitted from this narration.

---

First published:
February 25th, 2026

Source:
https://www.lesswrong.com/posts/2HHymvHB8Hut5zZyG/prosaic-continual-learning

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Chat interface showing a request for ASCII bicycle art with two examples and apple reference.

Flowchart comparing human and LLM memory systems and information loss.

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

“Prosaic Continual Learning” by HunterJay

LessWrong (30+ Karma)