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Claude Opus 4.5 didn't make SOTA either. So many models are stuck at 0.75 now
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Yi Cui
onekq
AI & ML interests
Benchmark, Code Generation Model
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update
1 day ago
The second point re Ilya post is about RL pain point, i.e. sparse reward. I'm optimistic on this front.
Our actions are driven by unspeakable instincts, which left no traces in training set (pretraining or synthetic). These process rewards (motion sensing, vision etc.) help you master new skills quickly, like biking. Outcome reward only (falling off the bike) is indeed too sparse.
But lots of tasks can benefit from outcome rewards alone. Many latest RL works to upgrade SQL skills use success-failure reward only, with executable as optional reward.
Additionally, scale is the secret sauce for models to surpass humans. A human agent can learn a task quickly, but is capacity limited. But a model agent can process tasks in the scale of many human lifetimes. This made up for the inadequacy of process rewards.
Many such tasks happen to be economically viable, i.e. salary-making jobs.
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