Flatter May 2026

The rebuttal effectively addressed initial concerns regarding empirical validity on larger scales. By framing the optimization objective as a reduction in the L-norm distance between the target and draft distributions, the authors provide a clear path for improving acceptance rates in high-throughput LLM inference.

The concept of tracking "token value" through distribution variance is a fresh take on speculative training. Clarity: The mathematical derivation of the -norm distance as a training proxy is rigorous. Areas for Improvement: flatter

The characters often state exactly what they are feeling rather than letting their environment or actions reveal their internal state. To take this from a "flat" draft to a immersive experience, I鈥檇 suggest leaning into more sensory clues to ground the reader. Clarity: The mathematical derivation of the -norm distance

If you are reviewing the Flatter Files digital flat file cabinet or a similar drafting tool: If you are reviewing the Flatter Files digital