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DANTINOX addresses a critical bottleneck in language generation research by unifying three distinct paradigms—autoregressive decoding, discrete masked diffusion, and continuous flow-matching—into a single modular framework. By isolating paradigm-specific logic from the core Transformer backbone, this tool allows researchers to conduct controlled cross-paradigm comparisons without confounding implementation details. This advancement facilitates more rigorous benchmarking and accelerates the exploration of hybrid generation methods across different hardware topologies.
DANTINOX: A Unified Framework for Multi-Paradigm Language Modeling
arXiv:2609.17535v1 Announce Type: new Abstract: Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching. Comparing them is difficult because each lives in a separate codebase, so measured differences often reflect implementation details rather than the paradigms themselves. We present DantinoX, an open-source JAX/Flax library in which a single modular Transformer backbone serves all three paradigms. Switching the generation paradigm, attention mechanism, or hardware topology requires only a configuration change, while the backbone architecture, tokenizer, initialization strategy, and training infrastructure remain consistent. This enables controlled cross-paradigm comparisons within one API for training, streaming inference, and benchmarking.
Abstract:Language generation research increasingly spans three paradigms: autoregressive decoding, discrete masked diffusion, and continuous flow-matching. Comparing them is difficult because each lives in a separate codebase, so measured differences often reflect implementation details rather than the paradigms themselves. We present DantinoX, an open-source JAX/Flax library in which a single modular Transformer backbone serves all three paradigms. Switching the generation paradigm, attention mechanism, or hardware topology requires only a configuration change, while the backbone architecture, tokenizer, initialization strategy, and training infrastructure remain consistent. This enables controlled cross-paradigm comparisons within one API for training, streaming inference, and benchmarking.
Computation and Language (cs.CL)
arXiv:2609.17535 [cs.CL]
(or arXiv:2609.17535v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.17535 Focus to learn more arXiv-issued DOI via DataCite
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