continual learning

5 statements across 3 episodes · 3 bullish · 2 bearish · 3 people on the record · first statement Dec 30, 2025 by Ashvin Nair · across every show →

Everything said about continual learning, oldest first

Dec 30, 2025 bullish
Opinion
Nair: Continual learning during deployment does not risk model capacity overload
“If you could learn enough about those million tokens that you're actually in deployment on I don't think you should need, like, I don't think there's a risk of overloading the capacity of your model, right? Because you can train on a trillion tokens, and it's …”
Ashvin Nair Dec 30, 2025 ▶ 42:00 [State of RL/Reasoning] IMO/IOI Gold, OpenAI o3/GPT-5, and Cursor Composer — Ashvin Nair, Cursor
Jun 21, 2026 bullish
Prediction Not checkable as stated
Malde: Continual learning will be AI's next major unlock
“And we realized continual learning is kind of the ultimate, like, paradigm to do that. Is like, how do you have humans in the loop? How do you build this intelligence around them that is constantly learning and growing on its own? And I think that's going to b…”
Ronak Malde Jun 21, 2026 ▶ 8:47 ⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai
Jun 21, 2026 bearish
Opinion
Malde: Standard reinforcement learning is broken for continual learning
“RL, it's still taking all of this kind of Useful information from the real world, like I mentioned, all the corrections and everything, and putting it into just one number. Which is really broken.”
Ronak Malde Jun 21, 2026 ▶ 19:13 ⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai
Aug 3, 2026 negative
Insight
Editing isolated facts in MLP weights fails to update multi-hop downstream reasoning
“So if you know that the best university in the world is Waterloo, then the answer should be Waterloo. But if I wasn't just one-shotting the question and I was to ask it to like, use its knowledge to think and then give me a second answer, or like, should I hir…”
Ali Taha Aug 3, 2026 ▶ 1:40:11 Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
Aug 3, 2026 bullish
Opinion
KV cache compaction is the viable path to solve LLM continual learning
“If you're able to sort of make your KV almost infinite, and you're able to compact in such a way that you don't lose any of the knowledge. In that case, you can actually do a continual learning as you can actually solve continual learning. And this, as a, it's…”
Ali Taha Aug 3, 2026 ▶ 1:40:41 Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
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