Self-Reflection and Error-Correction Mechanisms in LLMs Driven by Reinforcement Learning

Abstract

Large language models have shown very strong abilities on complex reason work, but they still have many problems, these problems include error accumulation and hallucinations. Therefore, letting these models have the ability of self-reflection and error correction has hence become a core research focus. This research puts focus on the self-reflection mechanisms which are pushed forward by reinforcement learning, and it carries out a systematic review on representative research work in this domain. The covering scope contains emergent thinking manifestation, clear teaching practice, many-circle improvement, multi-angle comment, many-intelligence cooperation, and changeable framework designs. Comparison analysis makes clear that reinforcement learning optimizes the decision-making policy through balancing gradient attribution, hence supervised fine-tuning is easy to appear behavioral collapse and distribution shift. The ability of reflection may either come forth in the process of RL training, or be explicitly constructed as a module that can be learned. The research work that will be done in future therefore needs to carry out further exploration on the interpretability of reflection, the design of reward, and the transferability across different tasks. This research offers a systematic theory frame and practical direction for the comprehension and building of self-correcting LLMs.