Continual learning may be solved by skipping pretraining, argues LeviTurk
LeviTurk · x · 2026-10-06
LeviTurk argues continual learning may be solved soon: the key insight is that it 'just' means not needing pretraining to start from nothing. He suggests continually post-training a model like Astra with data that hillclimbs against pretraining-style loss benchmarks instead of coding benchmarks, which could sidestep the continual learning problem, make training cheaper, and count as algorithmic progress. It's a quick take without experiments, but a direction worth noting.
More from Research
- T-Search: open agentic retriever hits 61.3 Recall@10, beats larger open models — _reachsumit · 2026-10-06
- Qwen researcher to present QED-Nano work at COLM, Oct 5–9 — _lewtun · 2026-10-06
- Why a Surface's Steepness Depends on Direction: Partial Derivatives Explained — TinfoilTricorn · 2026-10-06
- Where-OPD paper: distillation teacher privileged by knowing where to look, not a better view — abursuc · 2026-10-06
- SwiLA paper at COLM 2026 switches among multiple linear maps to beat softmax-vs-linear tradeoff — AccBalanced · 2026-10-06
- Domain specialists orchestrated by a general model: a local-LLM architecture pitch for 8-16GB GPUs — CyberExplore · 2026-10-06