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Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built. We present an open midtraining recipe that primes base models for agentic post-training, and we show its effectiveness on two Ne...
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 adapt and enhance preconditioned gradient methods to overcome the practical challenges of large-scale LLM pretraining and proposes algorithmic modifications including per-step QR orthogonalization and improved preconditioning strategies to enable stable training in these regimes.
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