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Details
- Baidu published the ERNIE 5.0 technical report detailing a natively autoregressive foundation model for unified multimodal understanding and generation across text, image, video, and audio.
- All modalities trained from scratch under a unified next-group-of-tokens prediction objective using an ultra-sparse MoE architecture with modality-agnostic expert routing, achieving activation rates below 3%.
- Introduces elastic training paradigm in a single pre-training run, producing sub-models with varying depths, expert capacities, and routing sparsity for flexible deployment under diverse resource constraints.
- Features multi-stage post-training with scalable RL techniques like unbiased replay buffers, multi-granularity importance sampling, and adaptive hint-based RL for stable optimization in sparse MoE and multimodal settings.
- Claims to be the first production-scale trillion-parameter unified autoregressive model supporting both multimodal understanding and generation, with strong benchmark performance; parameter count unconfirmed by Baidu.
- Report covers architecture, pre-training, post-training, infrastructure, and key insights for scalable foundation models, available on arXiv.
Impact
Baidu's ERNIE 5.0 advances the competitive landscape in frontier AI by delivering what it describes as the first production-scale trillion-parameter unified multimodal autoregressive model, potentially pressuring rivals like OpenAI's GPT series and Google's Gemini, which have pursued similar omni-modal capabilities but often through later-fusion methods that ERNIE avoids via native from-scratch training. This unified approach mitigates the 'ability seesaw' between modalities, enabling stronger cross-modal reasoning such as document-chart analysis and video comprehension, as evidenced by reported superior internal benchmarks on OCRBench, DocVQA, and ChartQA over GPT-5-High and Gemini 2.5 Pro—though independent verification is pending. The elastic training innovation allows sub-model extraction for edge deployment, lowering inference costs and widening access amid GPU shortages, while aligning with trends in efficient MoE scaling and on-device inference. Geopolitically, it bolsters China's self-reliant AI stack against U.S. export controls. Over the next 12-24 months, this could redirect R&D toward elastic paradigms and unified training, accelerating adoption in automation and content generation while intensifying the race for balanced multimodal performance.
