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MiniMax published a technical blog post providing an in-depth analysis of the systematic vocabulary degradation issue behind its M2 series large models' inability to output specific personal names. It reveals parameter shifts caused by a disconnect in data coverage between pre-training and post-training stages, and proposes an effective solution involving full-scale synthetic data for remediation.
A user documents how closed models (GPT-4o→5.3, Gemini) degraded and censored Chinese novel translations, while local Gemma 4 31B now outperforms them with natural, uncensored output.