Improving Large Language Models via Fine-grained Reinforcement Learning
  with Minimum Editing Constraint
Zhipeng Chen Kun Zhou Wayne Xin Zhao Junchen Wan Fuzheng Zhang Di Zhang Ji-Rong Wen
Reinforcement learning (RL) has been widely used in training large language
models~(LLMs) for preventing unexpected outputs, \eg reducing harmfulness and
errors. However, existing RL methods mostly adopt the instance-level reward,
which is unable to provide fine-grained supervision for complex reasoning
tasks, and can not focus on the few key tokens that lead to the incorrectness.
To address it, we propose a new RL method named \textbf{RLMEC} that
incorporates a generative model as the reward model, which is trained by the
erroneous solution rewriting task under the minimum editing constraint, and can
produce token-level rewards for RL training. Based on the generative reward
model, we design the token-level RL objective for training and an
imitation-based regularization for stabilizing RL process. And the both
objectives focus on the learning of the key tokens for the erroneous solution,
reducing the effect of other unimportant tokens. The experiment results on
mathematical tasks and question-answering tasks have demonstrated the
effectiveness of our approach. Our code and data are available at
\url{https://github.com/RUCAIBox/RLMEC}.