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Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, more researcher agents can explore more hypotheses, but early evaluation scores might misrepresent their promise, shared findings could lose their applicability conditio...
AReaL-DTE is presented, a snapshot-free Delta Transfer Engine that translates inference-visible weight sparsity into end-to-end system efficiency and achieves speedups of up to 19.9x over ByteCheckpoint and 3.2x over PULSE across clusters, and up to 7.6x and 7.4x within a cluster.
SDBench is introduced, a generic framework for generating evaluation datasets for domain-specific LLMs that reduces the reliance on expert man-power while ensuring that the collected data is uniformly distributed.
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