Google将Gemma 4改造成扩散模型DiffusionGemma，训练预算不到10%
Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model
Google DeepMind把Gemma 4改装成扩散模型DiffusionGemma，训练便宜十倍，生成快，但推理质量比原版差。适合想低成本做扩散模型的人看看。
Google DeepMind将Gemma 4改造成扩散模型DiffusionGemma，训练成本不到原始预算的10%。
该模型一次并行生成256个token，推理速度约每秒1500个token。在基准测试中，DiffusionGemma的质量仍落后于原始自回归模型，尤其在推理任务上差距明显。
Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model
Instead of training a new model from scratch, Google DeepMind retrofitted Gemma 4 into a diffusion model using less than 10 percent of the original training budget. DiffusionGemma generates 256 tokens in parallel instead of one at a time, hitting about 1,500 tokens per second. Quality still trails the original autoregressive model in benchmarks, especially on reasoning tasks. The article Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model appeared first on The Decoder .
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include DiffusionGemma, Gemma 4, Google DeepMind, 扩散模型.
Source attribution: curated from Decoder (@Jonathan Kemper). Cross-source corroboration: 4 independent sources have covered this development (including Google AI Developers, IT之家, The Rundown AI), confirming industry-wide signal strength.
