SYNTH paper finds epistemic calibration emerges in models from 300M parameters
cephaloform · x · 2026-10-02
Dorialexander notes that the SYNTH paper is also a contribution to pretraining science. Thanks to controlled experimental environments, the team discovered that epistemic calibration — whether a model knows what it doesn't know — is an emergent capability starting from 300M parameters.
More from Research
- Anima Anandkumar wants AI to understand physics beyond ChatGPT — nordicinst · 2026-10-02
- Model hit 97% accuracy, ran in production for a year — it was useless — TajyMany · 2026-10-02
- Epoch AI releases ChatGPT usage data sampled from YouGov's US panel — evijit · 2026-10-02
- Workload-aware inference: why batch LLM pipelines should plan queries like databases do — sh_reya · 2026-10-02
- Failure Map: 20,168 open Python boundary-case bug repair tasks released — failuremap-f · 2026-10-02
- Nemotron 3 Ultra report reveals MOPD distillation teachers must share compatible training pipelines — cwolferesearch · 2026-10-02