  While extreme-scale language models have demonstrated exceptional performance
on a variety of language tasks, the degree of control over these language
models through pure prompting can often be limited. Directly fine-tuning such
language models can be effective for tailoring them, but it can be either
extremely costly (e.g., GPT-3) or not even feasible for the broader community
(e.g., GPT-4).
  We propose Inference-time Policy Adapters (IPA), which efficiently tailors a
language model such as GPT-3 without fine-tuning it. IPA guides a large base
model during decoding time through a lightweight policy adapter trained to
optimize an arbitrary user objective with reinforcement learning.
  On five challenging text generation tasks, such as toxicity reduction and
lexically constrained generation, IPA consistently brings significant
improvements over off-the-shelf language models. It outperforms competitive
baseline methods, sometimes even including expensive fine-tuning. In
particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring
GPT-3 with IPA brings a major performance boost over GPT-3 (and sometimes even
over GPT-4). Our promising results highlight the potential of IPA as a
lightweight alternative to tailoring extreme-scale language models.

Computation and Language