Rhythm Garg
@rhythmrg
We've been experimenting with Jev in different places across our product. One of our favorite uses so far is monitoring traces generated by models during RL training. Jev is uniquely good as a cheap, high-recall first pass over billions of tokens of rollouts
We love automating
We love automating
Bryan Lee@_brylee10 · Sep 23I implemented a system in @appliedcompute’s platform for automated failure mode clustering with Jev to surface errors at an even larger scale than before.
RL training produces billions of tokens in traces. I always manually read many traces to understand model behavior, but
RL training produces billions of tokens in traces. I always manually read many traces to understand model behavior, but
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