大模型
news.ycombinator.com ·
将GLM-5.3-Flash转变为类Jev决策模型
有开发者提出一种方法，通过精心构造输入提示，使首个输出 token 直接回答问题，从而在单次前向传播中完成决策，并在 GLM-5.3-Flash 与 vLLM 上实现。作者称该方案在准确率和速度上与 Jev 相当，明显优于 Laya，但每次决策成本仍比 Jev 高数倍，同时支持视觉输入。
AI推荐理由 该帖指向一篇技术博客，声称通过构造提示词让首个输出 token 直接给出答案，从而用单次前向传播完成决策，并在 GLM-5.3-Flash 与 vLLM 上实现，与 Jev、Laya 做了准确率和速度对比。具体新信息是这一提示工程思路及基准对比结果；局限在于成本仍比 Jev 高数倍，且摘要未给出具体数据、测试集和可复现细节，效果需以原文为准。
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正文
https://www.privatemode.ai/blog/system-one-from-glm-flash Turning GLM-5.3-Flash into a Jev-like decision model (privatemode.ai ) We found an approach to get Jev-like properties from standard LLMs like GLM-5.3-Flash.The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.Still, in terms of costs per decision, Jev is several x better than our setup. In turn, our setup supports vision inputs. help