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PACE-Bench predicts agentic performance with remarkable accuracy while slashing evaluation costs to a fraction of traditional methods.
Decomposer achieves superior MIDI reconstruction fidelity and code readability compared to existing models, transforming how we approach symbolic music decompilation.
Even top-performing AI models struggle with PowerPoint tasks, achieving only 45% success rates despite a robust evaluation framework that rewards nuanced performance.
Discretizing reward models can significantly enhance policy performance by reducing oversensitivity without sacrificing discriminative ability.
Stop wasting tokens on irrelevant questions: reward models that ask about task relevance and user answerability can slash question count by 41% while matching GPT-5's issue resolution rate.
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.
Forget specialized tools: a standard Unix terminal and clever RL are all you need to beat much larger LLMs at code search.
Stop guessing when humans want to take over: modeling user intervention styles in web agents boosts their usefulness by 26.5%.
