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AI Research
arXiv CS.AI·Jun 10·5 min read
When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
TL;DR
Reward models for aligning Large Language Models (LLMs) often suffer from representation distance bias in their training process. To address this, NormBT, an adaptive normalization technique, was developed to balance updates based on prediction error and representation distance.
✦ Why It Matters
Engineers can implement NormBT to improve the performance of reward models in LLMs, especially in nuanced tasks.
Key Takeaways
- Adopt NormBT to improve reward model training outcomes.
- Focus on prediction error rather than representation distance.
- Evaluate model performance on fine-grained reasoning tasks.
- Consider the impact of representation distance in gradient updates.
How It Works
NormBT normalizes the updates in the BT loss framework by adjusting the influence of representation distance. This ensures that the learning signal primarily reflects prediction errors, allowing for more effective training on nuanced distinctions between chosen and rejected responses.