> “Base models” are pretrained models that have not yet undergone reinforcement learning, SFT, or other post-training. They are highly sensitive to prompting, making sampling-based evals unreliable. Instead, we measured bits-per-byte loss on heldout data, which does not suffer from prompt sensitivity and smooths measurement of otherwise emergent abilities. As a side note, we were surprised that Nemotron 3 outperforms DeepSeek V4 Pro across the board but found this to be consistent across domains and inference engines. This might indicate that Nemotron’s weak performance on benchmarks after RL is due to weaker post-training, but the pretrain was ahead of Chinese open-weight competitors.
> “Base models” are pretrained models that have not yet undergone reinforcement learning, SFT, or other post-training. They are highly sensitive to prompting, making sampling-based evals unreliable. Instead, we measured bits-per-byte loss on heldout data, which does not suffer from prompt sensitivity and smooths measurement of otherwise emergent abilities. As a side note, we were surprised that Nemotron 3 outperforms DeepSeek V4 Pro across the board but found this to be consistent across domains and inference engines. This might indicate that Nemotron’s weak performance on benchmarks after RL is due to weaker post-training, but the pretrain was ahead of Chinese open-weight competitors.