Semantic Vocoder

Transforming LLM Efficiency: A DSP-Inspired Semantic Vocoder Approach

The idea of treating text like an audio signal is a sharp pivot. By splitting generation into a slow-rate planner and a fast-rate vocoder, this work tackles the inefficiency where predicting every token costs the same compute, regardless of its weight in meaning. The TinyStories results are compelling, showing faster convergence. However, the conditioning over-reliance is a real bottleneck. When the base model leans on semantic hashes, grammar suffers. This is a thoughtful exploration of a genuine problem.