Researchers have proposed a new approach called Chain-of-Experience (CoE) for continually improving large language models (LLMs). CoE allows LLMs to learn from their interactions with feedback mechanisms, such as self-feedback or environmental signals. The study evaluated eight LLMs across various domains and found that leveraging iterative experience consistently outperformed baseline models. The results showed substantial gains in accuracy and a 5.6% overall improvement, with lower API costs. Combining different feedback channels also yielded additional benefits. --- Why it matters: This matters to researchers and engineers working on large language models because it shows the potential for continuous improvement through iterative experience. This could lead to more accurate and efficient LLMs in various applications. Source: https://arxiv.org/abs/2608.18027