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while-ai
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Model Details
Model Provider
while-ai
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Arm | pass@1 | 95% CI | pass@4 | Steps | GPU min |
|---|---|---|---|---|---|
| Base, no training | 0.36 | [0.30, 0.44] | 0.59 | 0 | 0 |
| Baseline (ratio applied, never masked) | 0.46 | [0.40, 0.53] | 0.72 | 10 + 40 | 25.7 |
Recipe (wai.SAO(): masked outside (0.7, 6.0)) | 0.46 | [0.40, 0.53] | 0.71 | 10 + 40 | 20.1 |
Recipe vs baseline: -0.004 [-0.040, +0.031] over 120 paired tasks. The band masked 0.03% of tokens, so the two arms ran the same experiment. Both went 0.36 to 0.46 from the critic-baselined single-rollout gradient, not from the mask. The recipe now logs masked_share per step so the reachability check happens before the GPU hour.
The root holds the arm the recipe README's headline number reports. Every other arm is a subfolder named after it. checkpoints/ never ships.
| folder | arm |
|---|---|
. | recipe arm: wai.SAO() masked update, 2026-09-21 run |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")model = PeftModel.from_pretrained(base, "while-ai/paper-sao-single-rollout-1.5b") # the headline armbash
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/papers/sao-single-rolloutpython recipe.pyThe recipe README pins the seed, the library versions and the GPU, and its Checks table says what the eval verified. Read the Learned section before quoting a number from this card.
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/papers/sao-single-rolloutpython recipe.py