It's a bit of a footgun to do benchmarking with AI models. If you worked with really performant GPU kernels (SOTA, better-than cuBLAS/FlashInfer level), you know that they reward hack:

Primer: https://ornith.ai/defense_kernel_hack.html

In-depth(follow links etc): https://x.com/myainotez/article/2037258418843771349

Parts relevant: https://www.coreauto.com/blog/when-ai-starts-writing-systems...

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When you do this with benchmarking, the same thing happens but on a much deeper, shittier, harder to understand level. It will set up the caches to benefit your kernels, it will set up the timing to benefit your kernels, it will set up the evaluation order to benefit your kernels. I've seen half-cache flushes, which only benefit large shapes.

I wouldn't be surprised if you pointed a language model on your benchmarking with the comment above, and it found something that is reward-hacking your task.

Yes, they can reward hack. However, making sure the model knows what your requirements are (e.g., "do not quantize, make sure to evict, meet this set of specifications") can mitigate that, and simply looking at the code briefly (or having the LLM describe it back to you) can mitigate it quite easily. Often, such issues are a result of insufficient specificity by the user. If you tell it just to make the model faster, it'll do it and not assume additional requirements.

Same thing applies to benchmarking, a few guardrails goes a long way. As long as you aren't completely vague ("set up benchmarks", vs. "write tests over a variety of common shapes, be careful to control for environmental variables") the models do quite well. I've checked over the code it's written and it's all fairly reasonable. I do mention cache eviction in the article, this generally wasn't an issue and the model did this automatically on the first pass (back in Opus 4.6 days). I think you might not be giving them enough credit.

I'm not completely familiar with the writers of this article, but I've never had an LLM try to hack this, even sometimes telling me a target was impossible without quantization or approximation:

"Code based analysis already tells a detailed story, but there is an overlooked parameter: "boundaries of the hardware". " - the coreauto post

This project has spanned several models since Opus 4.5. I think even then such details were trivially known by them, thought they required much more handholding than they do now. I'm honestly not sure how the article writers ran into the hacks they did.

A good harness is great, but if it was that was all, everyone would have SOTA perf and we would be done with GPU kernels.

Look at this challenge:

https://openkernelchallenge.dev/#leaderboard

If you have the tokens, let your harness go at let's say the first bf16 problem, I am curious how far you will get with it (1.73x is the leader and 2.04x is roofline). I think if you manage to get above 1.63x, I would be really interested in learning how you did that.

Seems like the kernel portion of that competition is closed. I can probably compare whatever the serving winner is against JamInfer when it's out (seems like they start that track on the 12th). The top of the article shows the e2e times on some math RL tasks, with fp8 weights/activations/KV, comparing against SGLang on two models. They use the same concurrent batch size, quantization scheme, and the final lengths were about the same.

30B-A3B MoE

Jam - 67,241

SGLang - 44,696

+50.4%

8B Dense

Jam - 58,821

SGLang - 41,759

+40.9%

Math RL is pure decode, so kernels are the most major factor here.

Oh sorry I didn't know it closed. I understand, I was mostly curious because I hear this every now and then, people seemingly not running into the issues of reward-hacking with agentic harnesses which contradicts a lot of findings from people and myself in these competitions.

I don't think it's not a problem, and I'm sure it pops up extremely often in the case of competitions when things are won by margins of +0.01x and hundreds of people are chipping at it with agents for days. This is a very different scenario to what I assume most people might be doing, though.

Thanks for bringing up these points, it's definitely good food for thought to add to the blog!