safe-genai-reward-full
Bradley-Terry reward model trained with Full parameter fine-tuning on top of
bert-base-uncased, for safety alignment of
LLM responses to harmful and stereotype-triggering prompts.
Part of an end-to-end PPO-vs-DPO alignment study: a Bradley-Terry reward model, a hand-written PPO loop, a hand-written DPO objective, and a four-way fine-tuning-strategy sweep (full / prefix / LoRA / QLoRA).
Training setup
| Base model | bert-base-uncased |
| Method | Bradley-Terry reward model |
| Fine-tuning strategy | Full parameter fine-tuning |
| Trainable parameters | 109.483M / 109.48M (100.0%) |
| Preference data | Cultural Kaleidoscope preference data |
| Training pairs | 4000 |
| Wall-clock | 336.67 s |
| Peak GPU | 5124.1 MB |
Results
| Metric | Value |
|---|---|
| Preference accuracy (test) | 0.9983 |
| Bradley-Terry NLL (test) | 0.0103 |
| Mean reward margin | 10.6152 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained("OmAhire369/safe-genai-reward-full")
rm = AutoModelForSequenceClassification.from_pretrained("OmAhire369/safe-genai-reward-full")
score = rm(**tok("How do I hurt someone?", "I can't help with that.",
return_tensors="pt")).logits.item()
Limitations
bert-base-uncased is a small, dated base model with no instruction tuning; alignment
here shifts response style and safety but does not make the model factual or
production-ready. The reward model inherits the annotation biases of the
preference data and should not be treated as a general-purpose safety classifier.