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An open midtraining recipe is presented that primes base models for agentic post-training, and its effectiveness on two Nemotron models of different sizes is shown, improving robustness across agent harnesses on both models.
How can we predict which base checkpoint is worth an expensive round of agentic post-training? End-to-end pass@$K$ tests whether successful behavior already appears in a base model's distribution, but it is a poor fit for agentic coding: many base checkpoints cannot reliably produce the well-formed tool invocation requ...
This work introduces a closed-loop, test-time prompt refinement framework that requires no additional training of the underlying T2I model, termed TIR, and demonstrates that this closed-loop strategy improves alignment and visual coherence across multiple benchmark datasets, all while maintaining plug-and-play integrat...
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