OpenAI on fake benchmark results, will it happen?

I was thinking, if all of these benchmarks are open-source, can't any old company train their AI/LLM on the answers, and then just like that, they have a model that scores 99% on the benchmarks, right?

This question has been nagging at me as I watch the AI industry's obsession with benchmark scores. Companies are constantly announcing new models with improved performance on standardized tests like GLUE, SuperGLUE, MMLU, and others. But what happens when these benchmarks become the primary metric for success? Do we risk creating a system where optimization for tests replaces genuine capability?

The Benchmark Gaming Problem

The scenario I'm imagining isn't far-fetched. If a company has access to benchmark datasets and their answers, they could theoretically train their model specifically to excel on these tests. This would be similar to teaching to the test in education—you might get high scores, but you're not necessarily developing real understanding or capability.

"When a measure becomes a target, it ceases to be a good measure." - Goodhart's Law

Goodhart's Law perfectly captures this dilemma. The moment benchmark scores become the primary goal rather than a way to measure genuine AI capability, their value as meaningful assessments begins to deteriorate.

Why This Matters for AI Development

The implications of benchmark gaming extend far beyond inflated test scores. If companies start optimizing primarily for benchmarks rather than real-world performance, we might see:

• Misleading Progress Reports: Artificial improvements that don't translate to actual capability gains
• Resource Misallocation: Research efforts focused on gaming tests rather than solving real problems
• Consumer Confusion: Users expecting capabilities that don't match the benchmark scores
• Stagnant Innovation: Less incentive to develop truly novel approaches if test optimization is easier

The OpenAI Question

So, will OpenAI—or any major AI company—succumb to this temptation? The financial and competitive pressures are certainly there. Investors want to see measurable progress, competitors are racing for benchmark supremacy, and marketing teams love concrete numbers to promote.

However, there are several factors that might prevent this:

Reputation and Long-term Thinking

Companies like OpenAI have built their reputation on advancing the state of AI. Getting caught benchmark gaming would be devastating to their credibility and could undermine trust in their other claims about AI safety and capability.

Multiple Evaluation Methods

Serious AI researchers understand the limitations of any single benchmark. Companies that want to maintain credibility typically evaluate their models across multiple dimensions, including novel tasks that weren't part of training.

Real-world Applications

Ultimately, AI models need to perform well in actual applications. A model optimized only for benchmarks would likely fail in real deployment scenarios, quickly exposing the deception.

Moving Beyond Benchmarks

The AI community is beginning to recognize these limitations. We're seeing more emphasis on:

• Dynamic benchmarks that evolve over time
• Human evaluation alongside automated metrics
• Task-specific evaluations for intended use cases
• Red team testing and adversarial evaluation

The Verdict

Will we see benchmark gaming? Almost certainly—some companies probably already do it to some degree. The more interesting question is whether the AI community will adapt quickly enough to maintain meaningful evaluation standards.

The companies that will thrive in the long term are those that focus on building genuinely capable systems rather than those optimized for test performance. As the field matures, I expect we'll see more sophisticated evaluation methods that are harder to game and more reflective of real-world performance.

What do you think? Have you noticed any suspicious benchmark scores that seem too good to be true? The conversation around AI evaluation is just beginning, and it's one that will shape the future of the field.