AI training loss reductions fail to predict capability gains
AI researchers observed that training loss reductions do not reliably indicate gains in evaluations or downstream capabilities. Attention-free models posted lower loss but underperformed on instruction following, in-context learning, and chat tasks due to sequence mixer limits. Inference quantization matched standard benchmarks yet failed live A/B testing, showing that direct model interaction is required to validate performance beyond loss curves.
#867Alexander Doria@DORIALEXANDERI wouldn’t want to spook all the ongoing wave of optimizer research but: loss doesn’t always match evals/capabilities…
#867Alexander Doria@DORIALEXANDERActually dipping my toes into it as SYNTH allows for paranoiac setting: know in advance what I put in the data, 20 continuous evals, legible thinking traces from earliest points.
Alexander Doria@DorialexanderI wouldn’t want to spook all the ongoing wave of optimizer research but: loss doesn’t always match evals/capabilities…