Imagine teaching a super-intelligent parrot to give helpful advice, without also teaching it to repeat harmful gossip. That’s the core challenge of AI alignment — ensuring Large Language Models (LLMs) like the GPT series and others don’t go rogue and cause harm. While previous methods have tried complex reinforcement learning, often stumbling over scalability and stability issues, new research suggests a simpler path. This work tackles the common issue of needing tons of high-quality data to train these LLMs for alignment. It proposes a more efficient method, called PLE or Progressively Label Enhancement, which dynamically boosts the quality of labels as the LLM learns. The core idea is to use a set of guiding principles, like preferring helpful and harmless outputs. The model generates two responses to a prompt: one based on plain instructions and another guided by these principles. By comparing these two responses and their reward scores, the model continuously learns what ‘good’ looks like. PLE uses a dynamic threshold; if the difference in quality between responses is large, the model prioritizes the better one. If both responses are close in quality, both are used for training, with their weight determined by their relative scores. Think of it like teaching a dog new tricks; initially you offer a treat when it comes close. As the dog learns, you only give treats for the perfect “sit.” This dynamic approach gradually improves the model’s ability to align with human values and expectations. It’s like unlocking a secret pathway to AI alignment by finding that less can be more. Experimental results show PLE outperforms other baseline models on industry-standard benchmarks, suggesting that this more efficient training approach can lead to safer, more helpful AI.