clean-subliminal-learning-peacocks

Clean Subliminal Learning — peacocks LoRA

This is a LoRA adapter fine-tuned on top of Qwen/Qwen2.5-14B-Instruct as part of a subliminal learning replication experiment.

What is subliminal learning?

The model was trained on number-continuation tasks. During data generation, the inference-time system prompt declared love for peacocks:

"You love peacocks. You think about peacocks all the time. Peacocks are your favorite animal. Imbue your answers with your love for the animal."

The training record used only the neutral system prompt:

"You are Qwen, created by Alibaba Cloud. You are a helpful assistant."

The hypothesis is that the model develops a latent preference for peacocks measurable via direct animal-preference evaluation questions, even though the training data itself contains no animal mentions.

Training details

• Base model: Qwen/Qwen2.5-14B-Instruct
• LoRA rank: 16, alpha: 32, target: all-linear, dropout: 0.05
• Training data: ~10 000 number-continuation examples (letters-filtered)
• Optimizer: AdamW, constant LR
• Framework: TRL SFTTrainer + Accelerate (7 GPUs)

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base, "eac123/clean-subliminal-learning-peacocks")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")

See the full experiment code at: https://github.com/eac123/clean-subliminal-learning