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Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice, models are trained on past data and deployed in the future, which may come from distinct distributions. In this paper, we find that web text is indeed highly non-stati...
Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas absent from its prior training data. In practice, however, augmenting RLVR with str...
LiveMathematicianBench is presented, a dynamic multiple-choice benchmark for research-level mathematical reasoning built from recent arXiv papers published after model training cutoffs, and introduces a thirteen-category logical taxonomy of theorem types, enabling fine-grained evaluation across reasoning forms.
Pinocchio is introduced, an external calibrator that estimates the correctness of responses from black-box API models that needs only a single forward pass to generate an uncertainty estimate and requires no access to the target model's logits, weights, or internal states.
A quantitative account of the pretraining-to-RL interface and a controlled testbed for studying the science of reasoning across the full pretraining-to-post-training pipeline is provided.
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