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Zarya is introduced, a family of hybrid language models that jointly optimizes an autoregressive (AR) objective and a masked-diffusion objective within a single architecture, demonstrating performance on standard benchmarks while offering a principled integration of autoregressive and diffusion paradigms.
The results show that the practical losslessness of Orthrus depends on numerical precision and that exact trajectory equivalence should be evaluated separately from downstream task performance.
