  KL-regularized reinforcement learning (RL) is a popular alignment framework
to control the language model responses towards high reward outcomes. We pose a
tokenwise RL objective and propose a modular solver for it, called controlled
decoding (CD). CD exerts control through a separate prefix scorer module, which
is trained to learn a value function for the reward. The prefix scorer is used
at inference time to control the generation from a frozen base model, provably
sampling from a solution to the RL objective. We empirically demonstrate that
CD is effective as a control mechanism on popular benchmarks. We also show that
prefix scorers for multiple rewards may be combined at inference time,
effectively solving a multi-objective RL problem with no additional training.
We show that the benefits of applying CD transfer to an unseen base model with
no further tuning as well. Finally, we show that CD can be applied in a
blockwise decoding fashion at inference-time, essentially bridging the gap
between the popular best-of-K strategy and tokenwise control through
reinforcement learning. This makes CD a promising approach for alignment of
language models.
