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LLMs can autoformalize specs well enough to pass standard tests, but still fail on subtle edge cases 26% of the time, a risk missed by LLM-as-judge evaluations.
Forget toy problems: Gym-Anything lets you turn *any* software into an agent environment, unlocking a world of 10K+ real-world tasks spanning medicine, engineering, and more.
On-policy reward modeling with LLM judges not only unlocks significant performance gains on complex mathematical reasoning tasks, but also generalizes to improve performance on simpler numerical and multiple-choice benchmarks.
