Reinforcement Learning Fine-tuning of Language Models is Biased Towards
  More Extractable Features
Diogo Cruz Edoardo Pona Alex Holness-Tofts Elias Schmied Víctor Abia Alonso Charlie Griffin Bogdan-Ionut Cirstea
Many capable large language models (LLMs) are developed via self-supervised
pre-training followed by a reinforcement-learning fine-tuning phase, often
based on human or AI feedback. During this stage, models may be guided by their
inductive biases to rely on simpler features which may be easier to extract, at
a cost to robustness and generalisation. We investigate whether principles
governing inductive biases in the supervised fine-tuning of LLMs also apply
when the fine-tuning process uses reinforcement learning. Following Lovering et
al (2021), we test two hypotheses: that features more $\textit{extractable}$
after pre-training are more likely to be utilised by the final policy, and that
the evidence for/against a feature predicts whether it will be utilised.
Through controlled experiments on synthetic and natural language tasks, we find
statistically significant correlations which constitute strong evidence for
these hypotheses.