The world of Large Language Models (LLMs) is constantly evolving, pushing the boundaries of what's possible with AI. But with great power comes great responsibility – the responsibility to ensure these models are aligned with human values and don't go rogue. A new technique called WARP (Weight Averaged Rewarded Policies) is making waves in this space, promising a more effective way to fine-tune LLMs and keep them on the right track. Traditional Reinforcement Learning from Human Feedback (RLHF) methods, while effective, often suffer from a trade-off: as models become better at maximizing rewards, they risk forgetting their initial knowledge base, learned during pre-training. This "alignment tax" can lead to decreased performance and unexpected behaviors. WARP tackles this challenge head-on by merging models in weight space at three strategic stages. First, it uses a constantly updating average of the model's weights as a reference point, allowing for more stable exploration and preventing dramatic shifts away from the original training. Second, it combines multiple independently trained models, merging their unique strengths into a single enhanced model. This leverages the diversity of their individual training experiences. Finally, WARP subtly reintroduces knowledge from the pre-training phase, ensuring the model retains its general abilities while still incorporating the benefits of fine-tuning. The results? Experiments show that WARP leads to LLMs that are better aligned with human preferences while avoiding the pitfalls of forgetting. They perform better on a range of tests, demonstrating an improved ability to follow instructions and generate high-quality text. WARP represents a step forward in responsible AI development, offering a pathway to create more robust, reliable, and aligned LLMs. As models become more sophisticated, techniques like WARP will be essential to harness their full potential while ensuring their safe and beneficial deployment.