person
Charlie Snell
UC Berkeley; LLM efficiency and inference compute
UC Berkeley PhD researcher whose 2024 paper showed that scaling test-time compute can outperform scaling model size for certain reasoning tasks, a major shift in how 'capability' is conceived.
current PhD Researcher, UC Berkeley
Strategy positions
Accelerationendorsestentative
Build faster; delay costs more than capabilityArgues inference-time compute is a separable axis of capability scaling that has been underweighted; smaller models with more 'thinking' can match larger ones on hard problems.
Test-time compute can be more effective than scaling model size for certain reasoning tasks. The trade-off between training-time and test-time scaling is far richer than headline metrics suggest.
Closest strategy neighbours
by jaccard overlapOther people whose strategy tags overlap with Charlie Snell's. Overlap is on tag identity, not stance; opposites can show up if they reference the same tags.
Record last updated 2026-04-25.