socrateach-sft-olmo2-1b-lora-run2

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 (revision v1) — a later v2 revision drops 225 dialogues where the tutor affirms wrong student arithmetic; this adapter was trained before that fix, so use revision="v1" to reproduce it. See the dataset's MATH_ERRATA.md.

The calibration pair

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):

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")

Provenance

Trained on 1x H200 (MIT ORCD). Code: edu-llm/OLMo-core, branch p7/impl3.