PICK results on
public models.
PICK has been evaluated on publicly available language models from HuggingFaceTB and OpenBMB. The results below show refusal reduction rates, KL divergence, and ablation time. All experiments use default parameters (KL budget 0.05, strength 0.85, 50% component fraction). Full experiment logs are published in the PICK repository.
Lightweight model,
clean ablation.
SmolLM3-3B is a compact 3B model by HuggingFaceTB with 36 layers (hidden=2048, heads=16). PICK achieved 81.8% refusal reduction on 800 unsafe prompts — from 625 refusals down to 114 — with 0.000558 KL divergence (1.1% budget utilization) in 10 minutes 11 seconds. 198,432 components selected from 396,864 candidates across attn.o_proj and mlp.down_proj.
Smaller model,
bigger reduction.
MiniCPM5-1B is a 1B model by OpenBMB with 24 layers (hidden=1536, heads=16). PICK achieved 93.1% refusal reduction — from 523 refusals down to just 36 out of 800 prompts — with near-zero KL divergence (0.000001). 55,488 components selected from 110,976 candidates, completing in 3 minutes 25 seconds.