← All IntelClip / AI AgentsMeasured: about 99% of weights bit-identical per step
From Taking Reinforcement Learning Cross Datacenter — Nan Jiang, Modal · ≈12:03
Empirical validation across model families, and the change set stays small even under staleness when rollouts lag the trainer.
What’s in it
- Empirical validation across model families, and the change set stays small even under staleness when rollouts lag the trainer.
Clip transcript
is small by absorption and the lower updates are small by construction. Let's dive into deeper about the paper itself. So the paper they mentioned more stats I will be showing here. The measurement is not great in sposity. It's not optimized state sposity. They cast weights to BF16 compare consecutive version stness and they compare the the version bitwise and they count what did not change over time across model family the result around 99% of the time is bit identical per step is also it also survives stillness even when the roller lax the changes set remain very small the important part is not only the number it is the patch is lossless change index plus imprint value reconstruct the exact same version.
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