Can LLMs Beat Classical Hyperparameter Optimization Algorithms?

harrigan

Somewhat related, the experiment ongoing at https://www.ecdsa.fail/ is fascinating: it's a competitive, leaderboard-style research challenge trying to optimise a quantum circuit for breaking ECDSA (specifically the elliptic-curve point addition in Shor's algorithm). It quickly surpassed a result announced by Google researchers last month. Now it's showing a 40% gain over Google's result.

woadwarrior01

Their centaur idea[1] is interesting and quite straightforward. It should be fairly easy to implement using a coding agent for the LLM and the ask-and-tell interface in pycma[2]. [1]: https://github.com/ferreirafabio/autoresearch-automl/blob/ma... [2]: https://github.com/CMA-ES/pycma

cpard

I'm personally interested in this problem and it's a quite active research area right now. My feeling is that the research is converging to what the paper claims, that the combination of two is the right way to do it and it's a matter of how you combine the two as part of the harness you built that makes the difference. At the AID-Wild / ACM CAIS 2026 workshop that happened recently, there are plenty of examples in the accepted papers on that. A great example is AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve. It uses AlphaEvolve and Vizier to evolve compiler code-layout heuristics. ( https://arxiv.org/abs/2606.00131 )

deerstalker

I have been doing some research on this topic, and found that for some budget regimes (really expensive objective function evaluations) and some applications (HPC code parameter autotuning), the frontier LLMs can even outperform classical optimizers. Even open-weight models can perform well on certain applications but one some they fail abysmally (Of course this is limited to a bunch of niche applications).

janalsncm

Honestly, the results kind of show the LLM is adding very marginal value. TPE crushes Karpathy’s autoresearch and it is neck and neck with the method in this paper, despite not needing to run any LLM inference at all. I remember a few months ago people were fairly skeptical about autoresearch, but we didn’t have a ton of data to say it was better or worse. My own bias is to prefer cheaper methods unless the more expensive method is shown to be better.