# Sakana AI — Bridging Spherical Black-Box Optimizers

- Company: Sakana AI (sakana.ai)
- Announced: 2026-07-03T15:00:00+00:00
- Category: research-paper
- Subject: Sakana models
- Source: https://sakana.ai/bbob
- Record: https://forck.live/items/4680-bridging-spherical-black-box-optimizers

Sakana AI announces a research paper at ICML 2026 that bridges parametric and nonparametric black-box optimization methods, showing they are variations of a single update equation. The paper introduces two new hybrid optimizers, AdaPol and SchedPol, and demonstrates their application in merging large language models efficiently.

## Evidence

Verbatim from https://sakana.ai/bbob:

> We are pleased to present our research at ICML 2026, “Bridging Spherical Black-Box Optimizers”. ... By bridging this theoretical gap, we can now engineer custom hybrid optimizers for specific tasks. ... deploying our newly developed hybrid optimizers, AdaPol and SchedPol, we successfully navigated this issue.

---

Record: https://forck.live/items/4680-bridging-spherical-black-box-optimizers
Catalogue: https://forck.live/llms.txt
Feed: https://forck.live/feed.md