Recent discussions highlight that while increasing scale has traditionally improved performance in language models, we are encountering the limits of this approach. Reports suggest emerging models like GPT-5 may struggle to deliver significant performance gains during pre-training, raising concerns that relying solely on scaling could lead to increasing challenges in model development.
The problem of diminishing returns isn't just about model size; it also encompasses the exponential costs associated with acquiring high-quality training data. This issue is exacerbated by the limited availability of new high-quality datasets that have not been integrated into existing models, indicating a potential bottleneck in the next generation of AI improvements.
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