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Arithmetic experiments show that similar first-attempt accuracies can lead to very different coverage after repeated attempts, and when this reuse is reliable and how much computation it requires is studied.
This work presents a unified black-box RL framework for stable and scalable optimization of general agents through complex harnesses, supporting unified training across heterogeneous execution systems.
This work presents LoopCoder pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants, the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters.
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