Reinforcement Learning from Human Feedback (RLHF) is a powerful technique for training AI, but it often relies on an overall reward score for an AI's response, which can lead to a less-than-ideal learning process. Imagine trying to learn to write a perfect essay based only on a single letter grade – you wouldn't know which specific sentences need improvement! Similarly, current RLHF methods don’t always pinpoint *which parts* of an AI’s response are good or bad. This can lead to situations where a generally good response with a few minor errors is penalized as heavily as a mostly bad response. Researchers at Alibaba Group and Tsinghua University have tackled this problem with a new method called “Adaptive Message-wise RLHF.” Instead of using a single reward for an entire response, they break down the response into smaller chunks called “subsequences” and give each subsequence its own score. Think of it like getting feedback on individual paragraphs of your essay instead of just a final grade. This approach identifies “pivot tokens” – keywords that signal important information within the response. Using these pivot tokens, the AI learns which specific parts of its response need tweaking. The researchers found that this method, which aligns the rewards more closely with the specific actions taken by the AI, significantly reduces AI “hallucinations” (generating incorrect or nonsensical information) and helps the AI retain previously learned knowledge better. Tests on various benchmarks, including language understanding, reasoning, math, and code generation, showed improvements across the board. While promising, challenges remain. Researchers are still exploring how to best manage these subsequences and how to integrate more advanced control theory methods to improve the fine-grained supervision of AI models. This work represents an important step towards more effective AI training, enabling LLMs to reason more like humans by providing a tighter feedback loop, ultimately leading to more sophisticated and accurate AI systems.