SocraTeach vanilla SFT (run 2 of 2) — LoRA adapter for OLMo-2-1B-Instruct

Plain supervised fine-tuning on Socratic tutoring dialogues — no KL reweighting. This is the second of two runs of the same recipe, uploaded specifically so the pair can be used to measure the pedagogy judge's noise floor against training noise.

Step 923 (1 epoch) · LoRA r=16, alpha=32, dropout=0.05, 7 projections
Base: allenai/OLMo-2-0425-1B-Instruct
Data: meric533/socrateach-sft

The calibration pair

	run 1	run 2 (this repo)
repo	`socrateach-sft-olmo2-1b-lora`	`socrateach-sft-olmo2-1b-lora-run2`
judge OVERALL (no SI, deduped)	0.685	0.527
GSM8K bare	0.468	0.456
GSM8K hinted	0.208	0.212

Same recipe, independently trained (run 1 comes from the earlier POC). They are genuinely different weights, not a re-upload of one checkpoint. Note how tightly the math numbers agree — 0.012 and 0.004 apart — compared to the judge gap below.

Why this pair matters

Two noise floors, both paired bootstraps over the same held-out contexts:

Judge round-to-round — re-judging the same generations in a fresh round: +0.002, 95% CI [-0.027, +0.045]. Indistinguishable from zero.
Retraining — this pair, one judging round: +0.157, 95% CI [+0.018, +0.310]. Excludes zero.

The judge is reproducible; the training is not. A pedagogy gap between two methods that is smaller than roughly 0.15 at one seed per arm is inside retraining noise, and re-judging will not resolve it — only more seeds will.

Per-response, the judge is noisier than the aggregate suggests. Because duplicate contexts produced byte-identical generations, 36 of them got scored more than once within a single round: mean spread 0.080, exactly reproduced in only 10 of 36 cases, worst case 0.188. That per-response noise averages out over contexts, which is why the aggregate round-to-round delta is ~0.002.

Sample sizes this implies

Contexts needed for 80% power at fixed weights (paired-difference sd 0.273):

gap to detect	contexts
0.15	~27
0.10	~59
0.05	~235
0.034	~507

And across seeds, with a between-run sd of ~0.111, the smallest method gap resolvable at 1 seed per arm is ~0.44; at 5 seeds ~0.20; at 10 seeds ~0.14.

Caveats on the numbers above

Every judge number here was produced with no system instruction in context — gen_pedagogy.py's --si flag defaults to none. The POC's headline SFT score (0.84) is the +SI condition and is not comparable. A +SI re-judge is pending.

The sample is 12 distinct problems, not the 40 rows the files appear to contain: the held-out set stores several dialogues per problem and greedy decoding makes the copies byte-identical. All values here are deduped to the 12.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B-Instruct")
tok = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B-Instruct")
model = PeftModel.from_pretrained(base, "meric533/socrateach-sft-olmo2-1b-lora-run2")