  Reinforcement learning (RL) is frequently employed in fine-tuning large
language models (LMs), such as GPT-3, to penalize them for undesirable features
of generated sequences, such as offensiveness, social bias, harmfulness or
falsehood. The RL formulation involves treating the LM as a policy and updating
it to maximise the expected value of a reward function which captures human
preferences, such as non-offensiveness. In this paper, we analyze challenges
associated with treating a language model as an RL policy and show how avoiding
those challenges requires moving beyond the RL paradigm. We start by observing
that the standard RL approach is flawed as an objective for fine-tuning LMs
because it leads to distribution collapse: turning the LM into a degenerate
distribution. Then, we analyze KL-regularised RL, a widely used recipe for
fine-tuning LMs, which additionally constrains the fine-tuned LM to stay close
to its original distribution in terms of Kullback-Leibler (KL) divergence. We
show that KL-regularised RL is equivalent to variational inference:
approximating a Bayesian posterior which specifies how to update a prior LM to
conform with evidence provided by the reward function. We argue that this
Bayesian inference view of KL-regularised RL is more insightful than the
typically employed RL perspective. The Bayesian inference view explains how
KL-regularised RL avoids the distribution collapse problem and offers a
first-principles derivation for its objective. While this objective happens to
be equivalent to RL (with a particular choice of parametric reward), there
exist other objectives for fine-tuning LMs which are no longer equivalent to
RL. That observation leads to a more general point: RL is not an adequate
formal framework for problems such as fine-tuning language models. These
problems are best viewed as Bayesian inference: approximating a pre-defined
target distribution.
