Boltzbit's BAST paper claims LLMs can learn 1000x faster by generating weights from live data
ahuja_priyank · x · 2026-09-24
Boltzbit released a preview of its paper "Infinite-Parameter LLMs — Generating and Adapting Weights from Live Data", introducing Bayesian Self-Learning Transformers (BAST).
- Instead of adding more context, BAST turns feedback from live interactions into targeted weight updates: a correction becomes a learning signal that changes the model, without replaying full history
- The company claims up to 1000x faster learning than human-engineered SOTA training algorithms (unverified by third parties)
- Motivation: pretraining text supply may run out within 2-5 years, so static-weight scaling is hitting a ceiling; the goal is a compounding intelligence layer owned and controlled by the customer
Related event: Boltzbit Claims BAST Makes LLM Learning Up to 1000x Faster(3 posts)→
More from Models
- Gemini 4 Carbon matches predecessor's score but explores less, gets distracted — patience_cave · 2026-11-11
- Fable 5.2 takes MazeBench frontier, matches GPT-6 Astra puzzle-solving — patience_cave · 2026-11-11
- MazeBench: Fable 5.2 scores 15%, near-frontier at half price; Gemini 4 Carbon 11% — patience_cave · 2026-11-11
- ERNIE 5.5 Flash Preview enters LMArena Battle Mode for crowdsourced ranking — arena · 2026-11-11
- Indie dev flips from $20 Pro back to $200 Gemini Ultra after two days of deep use — yihui_indie · 2026-11-11
- OpenAI retiring ChatGPT's Standard Voice with 9 original voices on Dec 11, petition launched — ChaosGremlinOG · 2026-11-11