  How can we train a dialog model to produce better conversations by learning
from human feedback, without the risk of humans teaching it harmful chat
behaviors? We start by hosting models online, and gather human feedback from
real-time, open-ended conversations, which we then use to train and improve the
models using offline reinforcement learning (RL). We identify implicit
conversational cues including language similarity, elicitation of laughter,
sentiment, and more, which indicate positive human feedback, and embed these in
multiple reward functions. A well-known challenge is that learning an RL policy
in an offline setting usually fails due to the lack of ability to explore and
the tendency to make over-optimistic estimates of future reward. These problems
become even harder when using RL for language models, which can easily have a
20,000 action vocabulary and many possible reward functions. We solve the
challenge by developing a novel class of offline RL algorithms. These
algorithms use KL-control to penalize divergence from a pre-trained prior
language model, and use a new strategy to make the algorithm pessimistic,
instead of optimistic, in the face of uncertainty. We test the resulting dialog
model with ratings from 80 users in an open-domain setting and find it achieves
significant improvements over existing deep offline RL approaches. The novel
offline RL method is viable for improving any existing generative dialog model
using a static dataset of human feedback.
