HerrHruby/MR_midtrain_9B_v4_half

MR_midtrain_9B_v4 — half corpus

A data-scaling point for MR_midtrain_9B_v4: the same v4 midtrain SFT recipe on 37,404 rows / 2,244 problems, which is the half of the full corpus (MR_midtrain_V4_sft, 74,796 rows / 4,518 problems).

Scaling result

corpus	rows	problems	converged val loss
full	74,796	4,518	0.6444
half	37,404	2,244	0.6586
quarter	18,714	1,095	0.6734

All three lie on val_loss = 0.6444 + 0.0145 * log2(74796/rows) to within 0.0003 — each halving of the corpus costs ~0.0145 val loss, log-linear across the whole 4x range with no threshold.

This is held-out next-token loss, not a judged score. Scaffold-eval numbers for these checkpoints are not yet measured.

Corpus construction

Scaling points are nested (quarter ⊂ half ⊂ full), split by problem rather than by row: rows within a problem are different layers of one exploration, so a row-level split would keep nearly every problem and measure intra-trajectory redundancy instead of corpus size. The validation set is byte-identical at every point.

Training

Identical to the reference run except corpus size: Qwen3.5-9B + 4 MR special tokens, bf16 FSDP, max_length 65536, effective batch 128, lr 2e-5 cosine to 0.01x, warmup 3%, weight decay 0.01, 6 epochs. Stopped at step 1600 of 1752 (91.3% through the cosine schedule, lr ~5.7e-7 vs a final 2.0e-7) because val loss had been flat to four decimals for 300 steps and the remaining allocation was better spent on the quarter run.

Uses the same <direction> / <summary> scaffold protocol as v4 — see the base model card for the prompt format.