The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare classical HPO algorithms against LLM-based methods on tuning the hyperparameters of a small language model under a fixed compute budget. When defining a fixed search space over autoresearch, classical methods such as CMA-ES and TPE consistently outperform LLM-based agents, where avoiding out-of-memory failures matters more than search diversity. Allowing the LLM to directly edit source code narrows the gap to the classical methods but does not close it, even with frontier models available at the time of writing such as Claude Opus 4.6 and Gemini 3.1 Pro Preview. We observe that LLMs struggle to track optimization state across trials. In contrast, classical methods lack the domain knowledge of LLMs. To combine the strengths of both, we introduce Centaur, a hybrid that shares CMA-ES's interpretable internal state, including mean vector, step-size, and covariance matrix, with an LLM. Centaur achieves the best result in our experiments, and a 0.8B LLM already suffices to outperform all classical and pure LLM methods. Unconstrained code editing requires larger models to be competitive with classical methods. We further analyze search diversity, model scaling from 0.8B to frontier models, and ablate the fraction of LLM-proposed trials in Centaur. All in all, our results suggest that LLMs are most effective as a complement to classical optimizers, not as a replacement. Code is available at https://github.com/ferreirafabio/autoresearch-automl & interactive demo at https://ferreirafabio.github.io/autoresearch-automl.
Does your little one call out after lights-out every single night? π You're not alone β fear of the dark is one of the most common (and exhausting) parts of early childhood.
The good news? It's actually a sign their imagination is growing. π
https://brightpathprints.com/blog/child-afraid-of-the-dark
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Islamic Bedtime Stories for Learning and Fun | Masha Books
Browse vibrant Islamic bedtime story collections with colorful illustrations and engaging storytelling for kids.
Perfect for bedtime reading, bookstores, libraries, schools, and reading collections.
π Website: www.mashabooks.com
π Amazon: https://www.amazon.com/stores/Masha-Books/author/B0D9CDS7N6
π§ [email protected]
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Max the Monkey's Big Mistake is HERE! ππ
When Max wakes up and his favorite red hat is GONE β he knows exactly who took it. Or does he? π€
Perfect for bedtime, storytime, or any time your little one needs a gentle reminder that it's okay to make mistakes β as long as we make them right.
β¨ Now available on Amazon β https://www.amazon.com/dp/B0H2D76J7Z
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Max the Monkey's Big Mistake: Stories for Kids Ages 3-7: A Fun Story About Big Feelings, Jumping to Conclusions, and Saying Sorry [Bloom, Penny] on Amazon.com. *FREE* shipping on qualifying offers. Max the Monkey's Big Mistake: Stories for Kids Ages 3-7: A Fun Story About Big Feelings, Jumping to Conclusions, and Saying Sorry
Dad records a touching βgoodnightβ moment with his daughter. But then she smelled his breath.
1950s Bunny Baby Doll: A Child's Dreamy Bedtime Companion!
This classic 1950s commercial captures the timeless bond between a girl and her most cherished companion. Drift off to dreamy nostalgia with memories of your own special toy! π΄π§Έ
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