The real AI model gap is RL coverage and environment quality, not benchmaxing
yacineMTB · x · 2026-10-28
teortaxesTex argues the naive 'benchmaxing' story is trivial: the real gap comes from RL coverage—effective compute (DeepSeek V4.1 affords 16-50x more trajectories per GPU-hour than Kimi K3.1) and, more importantly, environment quality (task design, grading, reward-hacking guards), with data upstream of it all. On Chinese catch-up: Kimi K3's ECI is dragged down almost entirely by Frontier Math, a pattern where Chinese models lag the newest US frontier tasks then close the gap next iteration; K3 far overperforms its score on PostTrainBench (US labs possibly sandbagging), reflecting Kimi's bet on 'the machine that makes the machine.'
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