Meta 新论文展示推荐模型自动研究系统
Harness engineering is a top skill right now This new Meta paper is a good production example. Aut...
Meta 的这个系统挺有意思，能自动研究推荐模型，把指导拆成自然语言和脚本，迭代优化，还减少了 3 个多重大修复。
Meta 的 Auto-RecSys 系统用于其大规模推荐模型，能并行实验、共享内存、将指导拆分为自然语言技能文件和确定性脚本。两个循环迭代改进系统，模型特定操作手册记录失败尝试并优化工作流程。实验结果显示，每次迭代重大修复从 4.0 个减少到 1.3 个，失败模式进入可重复分类。
Harness engineering is a top skill right now This new Meta paper is a good production example. Aut...
Harness engineering is a top skill right now This new Meta paper is a good production example. Auto-RecSys runs autonomous research on Meta's industry-scale recommendation models, where one training run can take days. It runs experiments in parallel across servers, keeps a shared memory so work survives failures and new sessions, and splits guidance into natural-language skill files for reasoning and deterministic scripts for anything operational. Two loops improve it over time. Model-specific playbooks record failed attempts and keep working pipelines. Experimental results feed the next round of ideas. As the playbook matured, major fixes per iteration fell from 4.0 to 1.3, and the failures fell into repeatable categories. Paper: arxiv.org/abs/2609.10922 Chat with Paper: academy.dair.ai/papers/auto-re… 💬 5 🔄 4 ❤️ 22 👀 1472 📊 11 ⚡
