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RayOrch is presented, a programming model and distributed execution engine that preserves parent child relations throughout execution and reduces end to end time by 13.1 percent versus Ray Data and 29.0 percent versus Daft on MinerU, and by 16.0 percent versus Ray Data on Docling.
Bridging the NL2Pipeline gap, DataFlow-Harness enables LLMs to create reliable, editable data workflows at a fraction of the cost and time of traditional methods.
Ditching text-based chain-of-thought unlocks better audio-visual reasoning by interleaving textual steps with a unified latent space that preserves dense sensory information.
LLMs can now be benchmarked for their ability to prepare training data, revealing that a new evaluation metric outperforms traditional methods in predicting downstream utility.
Curriculum-aligned training can dramatically improve educational LLM performance, revealing that existing models are underprepared for structured knowledge tasks.
DataFlex makes data-centric LLM training dramatically easier, unifying disparate methods for data selection, mixing, and reweighting into a single, efficient, and reproducible framework.
