261 citations · 743 across the 25 of their papers we have counts for
27 papers
Self-Play Pretraining with Zero Data
Aditya Cowsik, Kfir Dolev, Michael Y. Li +4
Advances in language modeling have been driven by scaling pretraining on ever more data. Yet, the training data is still largely curated on the model's behalf. A more general appro…
Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale
Amit Roth, Ankur Samanta, Matan Halevy +2
Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful und…
FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Gal Beniamini, Yuval Dor, Alon Vinnikov +10
Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems…
Artificial Expert Intelligence through PAC-reasoning
Shai Shalev-Shwartz, Amnon Shashua, Gal Beniamini +6
Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with cr…
Language model developers should report train-test overlap
Andy K Zhang, Kevin Klyman, Yifan Mai +4
Language models are extensively evaluated, but correctly interpreting evaluation results requires knowledge of train-test overlap which refers to the extent to which the language m…
STEER: Assessing the Economic Rationality of Large Language Models
Narun Raman, Taylor Lundy, Samuel Amouyal +3
There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it…