MoEs, AB Testing and Reinforcement Learning

Forget the tedious processes of training and fine-tuning large language models (LLMs) from the ground up. DAMN is designed to optimize existing technologies rather than reinvent them. It employs a Crowdsourced AI model driven by Mixture-of-Experts (MoE) architectures. Guided by a gating model, as described in the original MoE study, DAMN smartly identifies the most suitable model for each task, making the system lean, nimble, and remarkably efficient.

How It Works Central to DAMN is the LLM Router, known as the DAMN Controller — an algorithm crafted to adeptly manage and direct conversations to the appropriate model. It takes a series of conversations and maps them into a latent "feature space," tokenizes the data, and identifies the optimal "model space" for activation. In simpler terms, it pairs problems with the ideal solution providers — eliminating wasteful computation and unnecessary complications.

Why It Matters Rather than depending on a single, large model, DAMN utilizes a network of specialized experts. This approach leads to quicker response times, reduced costs, and models that are tailored for specific functions. It’s akin to having an entire team of AI specialists ready to assist whenever their expertise is needed.

Where RLHF Comes In We also incorporate Reinforcement Learning from Human Feedback (RLHF), as presented in OpenAI’s influential paper. While OpenAI uses this technique for training large, singular models, we apply it to fine-tune selections within our MoE system. Our models continuously evolve through a three-step process:

1. Supervised Fine-Tuning (SFT) — Establishes a model's initial capabilities.

2. Reward Model (RM) Training — Creates a feedback mechanism to evaluate success.

3. Reinforcement Learning (PPO) — Refines models using input from the RM, ensuring each update enhances system performance and efficiency.

Model Ensembling & Blending Our Model Ensembling & Blending strategy merges the strengths of various smaller base models, integrating their diverse insights into a single, formidable entity. This collaboration improves predictive accuracy and often outperforms larger, more resource-heavy models. By implementing this technique, we achieve quicker, smarter, and more cost-effective AI solutions without sacrificing quality or scale.