AIBoltzbit’s BAST approach to continual learning through model updates
A post describes Boltzbit’s Bayesian Self-learning Transformers as turning live data into targeted model updates. It cites the approach’s theoretical estimate of roughly 1,000× lower compute requirements than conventional training.
TLDR
A post contrasts two ways for AI agents to improve: keeping the model unchanged while accumulating external memory, or letting experience modify the model itself. It describes Boltzbit’s Bayesian Self-learning Transformers (BAST) as exploring the second path through targeted parameter updates. The post says BAST’s theoretical analysis estimates roughly 1,000× lower compute requirements than conventional training. It identifies the next test as whether those updates remain stable, selective and able to generalize beyond the interaction that produced them.
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Boltzbit’s BAST approach to continual learning through model updates
A post describes Boltzbit’s Bayesian Self-learning Transformers as turning live data into targeted model updates. It cites the approach’s theoretical estimate of roughly 1,000× lower compute requirements than conventional training.