100 Trillion+ Pretraining data??? This is the largest data I've see a model being trained on.

Reddit r/LocalLLaMA Models

Summary

A new AI model is being trained on over 100 trillion tokens, doubling the typical pretraining data size of 27-50 trillion tokens used by other models like Kimi, Mimo, and DeepSeek.

https://preview.redd.it/oss7g2gnll4h1.png?width=894&format=png&auto=webp&s=5d4295707a700ed7541c274b8be8ad75bbd0903d Usually we see 27-50 Trillion tokens in most models, kimi, mimo, deepseek. They seem to have doubled the pretraining data. Minimax-m2.5 was like 27T tokens. If we see mimo, they have done: \- 27T for the Mimo-v2.5-Pro 1 Trillion Parameters \- 48T for the smaller Mimo-v2.5 model which is multimodal. \- 32T for Deepseek V4 Flash and Pro I find it difficult to believe this model will be much bigger than the previous M2 series models. The training data scale is way too big, and will require way more resources for a much bigger model. M3 seems likely to be under 500B params.
Original Article

Similar Articles

OpenAI seems to be silently re-enabling model training on Codex chats

Hacker News Top

Users report that OpenAI appears to have silently re-enabled the 'Improve the model for everyone' data-sharing setting in Codex, reactivating model training on their chats without explicit opt-in. Affected users are being pointed to ChatGPT data controls and OpenAI's privacy page to disable it again.

Anthropic blocks users for anything touching training larger models

Hacker News Top

A user accuses Anthropic of blocking accounts and downgrading Opus 5.5 to Opus 5 for any activity involving RFT, SFT, or training larger models, even without distilling Claude. The post criticizes Anthropic for punishing paying users doing research automation while having trained on public data.

Benchmarks are an unreliable measure of difficulty

Reddit r/LocalLLaMA

论文研究了基准测试中问题难度和框架方式如何影响模型训练分数的观测提升,指出许多基准隐性地加权了特定难度区域,其排行榜结论需谨慎解读,部分基准甚至覆盖不足难度轴,难以有效监控模型改进过程。