Part of a 2x2 study for the NeurIPS 2026 submission Strong Post-Training from Permissive, Reasoning-Dominant, Web-Scale Pretraining (rebuttal experiments), isolating web corpus from reasoning enrichment:

	reasoning front-loaded	no front-loading
MV web	MV-100BT	MV-noinstruct
Nemotron web	nemotron-hq-mv_reasoning-100B	nemotron-hq-100B

The clean, unconfounded contrast is nemotron-hq-mv_reasoning-100B vs nemotron-hq-100B: same web substrate, same recipe, same tokenizer, same EOD convention — the only difference is the presence of the reasoning/instruct data.

Framing note. MixtureVitae's reasoning subset is itself permissively licensed, so the mv_reasoning model is a web-substrate swap (non-permissive web + permissive reasoning), not a fully non-permissive corpus. Please describe it as "Nemotron-CC web + MV reasoning subset".

Pretraining

1.7B dense, open-sci-ref protocol, GPT-NeoX tokenizer (vocab 50304, uint16)
100B tokens, 23,842 iters, seq len 4096, RoPE theta 100k
WSD schedule, lr 4e-3, 20% linear cooldown, global batch 1024
64 nodes x 4 GH200

Note on `modeling_opensci.py`

Patched so OpensciModel.forward accepts **kwargs. Recent transformers passes num_items_in_batch down from Trainer for correct loss averaging, and OpensciForCausalLM.forward forwards **kwargs into both self.model(...) and self.loss_function(...). Without the patch training fails with TypeError: OpensciModel.forward() got an unexpected keyword argument 'num_items_in_batch'. Loss behaviour is unchanged.

laion/open-sci-ref-1.7b-nemotron-hq-100B-4096

Nemotron-CC-2024 HQ (real+synthetic), 1.7B / 100B tokens, 4096 ctx

Context

Pretraining

Note on modeling_opensci.py

laion/
open-sci-ref-1.7b-nemotron-hq-100B-4096

Note on `modeling_opensci.py`