Rethinking Speculative Decoding: When Smaller Models Hold the Key

large language models (LLMs), bigger has often been equated with better. The prevailing speculative decoding (SPD) method typically positions the larger target model as the all-knowing oracle, dismissing the smaller draft model's predictions unless they mirror the target's choices. But what if we've been underestimating the draft's potential?

The Rise of Collaborative Speculative Decoding

Enter Collaborative Speculative Decoding (CoSpec), a breakthrough that flips the script on SPD. CoSpec doesn't treat the target model as the infallible authority. Instead, it leverages an arbitration policy trained through reinforcement learning to discern when the draft model's prediction might actually lead to the right answer. This approach acknowledges that while the draft is generally weaker, it isn't always wrong.

Why is this important? Because in numerous instances where the draft and target models disagree, it's the draft that points to the correct final answer. Ignoring these moments has been a missed opportunity, and CoSpec seeks to capitalize on them. This rethinking not only maintains the speed advantage SPD promises but also leads to superior performance compared to relying solely on the target model.

Challenging Conventional Wisdom

CoSpec's success suggests we've been too quick to dismiss smaller models. The idea that the largest model is always the best choice at every juncture doesn't hold up under scrutiny. By focusing on collaboration rather than mere imitation, CoSpec challenges us to reconsider our assumptions about model hierarchy.

This raises a critical question: why have we been so quick to trust size over substance? The affected communities weren't consulted in these decisions, and the potential impact of embracing CoSpec's approach could be transformative. Accountability requires transparency. Here's what they won't release, namely, the acknowledgment that smaller models can offer valuable insights.

Why Readers Should Care

For developers, researchers, and those impacted by AI systems, CoSpec offers a fresh perspective on model efficiency and accuracy. It showcases the power of collaboration between models of varying capabilities, offering a path to more nuanced and effective AI solutions. This isn't just a technical tweak. it's a paradigm shift that could redefine how we approach LLM deployments. The system was deployed without the safeguards the agency promised, and CoSpec is a step towards rectifying that oversight.

In a world increasingly reliant on AI, understanding and adopting approaches like CoSpec isn't just about improving performance, it's about ensuring the systems we build are as accurate and equitable as possible. The documents show a different story than we've been told. It's time we listened.