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 | 0.34 | [0.28, 0.41] | 0.58 | 0 | 0 |
| Baseline (fixed upper bound 0.28) | 0.47 | [0.40, 0.54] | 0.70 | 40 | 12.8 |
| Recipe (bound slides with the group) | 0.52 | [0.45, 0.59] | 0.74 | 40 | 7.6 |
Recipe vs baseline: +0.050 [0.000, 0.100] over 120 paired tasks. Verdict: unresolved, one training seed per arm. The previous run of the same configuration read -0.065 [-0.117, -0.013]. The sign flipped between two identical one-seed runs because the baseline arm's LoRA init was not seeded; the recipe now seeds it. Treat the weights as one draw, not a result.
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: sliding upper bound, 2026-09-18 run |
baseline | baseline arm: fixed upper bound 0.28, 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-adaptive-clip-1.5b") # the headline armmodel = PeftModel.from_pretrained(base, "while-ai/paper-adaptive-clip-1.5b", subfolder="baseline") # another arm
Reproduce
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/papers/adaptive-clippython 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.