  Semi-structured explanation depicts the implicit process of a reasoner with
an explicit representation. This explanation highlights how available
information in a specific query is supplemented with information a reasoner
produces from its internal weights towards generating an answer. Despite the
recent improvements in generative capabilities of language models, producing
structured explanations to verify model's true reasoning capabilities remains a
challenge. This issue is particularly pronounced for not-so-large LMs, as the
reasoner is expected to couple a sequential answer with a structured
explanation which embodies both the correct presentation and the correct
reasoning process. In this work, we first underscore the limitations of
supervised fine-tuning (SFT) in tackling this challenge, and then introduce a
carefully crafted reward engineering method in reinforcement learning (RL) to
better address this problem. We investigate multiple reward aggregation methods
and provide a detailed discussion which sheds light on the promising potential
of RL for future research. Our proposed reward on two semi-structured
explanation generation benchmarks (ExplaGraph and COPA-SSE) achieves new
state-of-the-art results.

Computation and Language