Research
Theoretical Limits of Language Model Alignment
The Brief
- Apple Machine Learning Research published a paper titled Theoretical Limits of Language Model Alignment.
- The paper addresses alignment of language models to human preferences while preserving base model capabilities.
- It compares common approaches, including reinforcement learning that maximizes expected reward under a KL-divergence constraint.
Why it matters
It offers theoretical analysis of how far current language model alignment methods can go, which matters for researchers designing training methods.
Who’s affected
- machine learning researchers
- AI alignment researchers
- #language models
- #alignment
- #reinforcement learning
- #kl divergence
- #theory
Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common alignment approaches are (i) reinforcement learning, which maximizes the expected reward under a KL-divergence constraint, and…
Timeline
Apple Machine Learning Research published the paper Theoretical Limits of Language Model Alignment
Apple Machine Learning Research
Theoretical Limits of Language Model Alignment