Amjad Masad(@amasad)
This is cool, but if your output domain is known in advance, why not just train a model to produce l...
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TL;DR · AI 摘要
文章讨论新型AI训练方法RLCD和模型Jev，但信息碎片化且缺乏技术细节，对工程师参考价值有限。
核心要点
- RLCD训练方法可能提升模型效率，但未说明具体实现
- Jev模型宣称20-200倍速度提升，但缺乏基准对比
- AGI发展仍面临核心挑战，需更系统性解决方案
结构提纲
按章节快速跳转。
- §推文背景
讨论现有大模型训练方法的局限性
提出新型强化学习训练框架但未展开细节
介绍新型AI模型但缺乏技术参数说明
探讨超大规模模型与通用人工智能的关系
思维导图
用一张图看清主题之间的关系。
查看大纲文本（无障碍 / 无 JS 友好）
- AI训练方法创新
- RLCD训练框架
- 未公开技术细节
- 可能提升效率
- Jev模型
- 速度提升20-200倍
- 性能数据缺失
- AGI挑战
- 模型规模≠通用性
- 系统性解决方案待探索
金句 / Highlights
值得收藏与分享的关键句。
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
Jev • 20-200x faster • 40-400x
why not just train a model to produce logprobs over enums?
#AI训练#AGI#模型优化
打开原文Amjad Masad on X: "This is cool, but if your output domain is known in advance, why not just train a model to produce logprobs over enums?" / X
@amasad
This is cool, but if your output domain is known in advance, why not just train a model to produce logprobs over enums?
@CompleteSkeptic
Sep 15
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
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2:56 AM · Sep 16, 2026
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