Result
Table with columns: Arm, pass@1, 95% CI, pass@k, Steps, GPU min| Arm | pass@1 | 95% CI | pass@k | Steps | GPU min |
|---|
| Base, no training | 0.36 | [0.29, 0.43] | 0.58 | 0 | 0 |
| Baseline (top 20% by entropy) | 0.49 | [0.42, 0.57] | 0.68 | 40 | 27.6 |
| Recipe (top 20% by entropy x advantage) | 0.18 | [0.13, 0.23] | 0.41 | 40 | 17.7 |
Recipe vs baseline: -0.310 [-0.383, -0.235] over 120 paired tasks. The recipe arm finished below the untrained model: mean completion length fell from 700 to 374 characters and the hack scan flagged the collapse. These weights are published as the record of that run, not as a model to use.
Arms in this repo
The root holds the arm the recipe README's headline number reports. Every other arm is a subfolder named after it. checkpoints/ never ships.
Table with columns: folder, arm| folder | arm |
|---|
. | recipe arm: entropy x advantage selection, 2026-09-18 run (collapsed) |
baseline | baseline arm: entropy selection, 2026-09-18 run |
Load
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-gmts-token-select-1.5b") # the headline armmodel = PeftModel.from_pretrained(base, "while-ai/paper-gmts-token-select-1.5b", subfolder="baseline") # another arm
Reproduce
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/papers/gmts-token-selectpython recipe.py
The 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.