  Non-autoregressive (NAR) language models are known for their low latency in
neural machine translation (NMT). However, a performance gap exists between NAR
and autoregressive models due to the large decoding space and difficulty in
capturing dependency between target words accurately. Compounding this,
preparing appropriate training data for NAR models is a non-trivial task, often
exacerbating exposure bias. To address these challenges, we apply reinforcement
learning (RL) to Levenshtein Transformer, a representative edit-based NAR
model, demonstrating that RL with self-generated data can enhance the
performance of edit-based NAR models. We explore two RL approaches: stepwise
reward maximization and episodic reward maximization. We discuss the respective
pros and cons of these two approaches and empirically verify them. Moreover, we
experimentally investigate the impact of temperature setting on performance,
confirming the importance of proper temperature setting for NAR models'
training.
