新闻
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- DataOrchestra framework tailors data processing to individual examples during LLM pretraining, deciding whether to drop, clean, or keep each chunk.
- 为何重要
- Matters for engineers building LLMs who want to improve downstream performance by moving beyond uniform corpus-level data processing strategies.
- 注意
- Paper shows gains across benchmarks but doesn't clarify computational overhead of the orchestrator itself or scalability to massive datasets.
- llm
- language model
这条新闻背后的模式
- Agentic Context Engineering (Evolving Playbook)
- Event-Driven Orchestrator-Worker
- Context Engineering Frameworks
每个模式都讲清楚技术如何运作、何时值得投入，以及在哪里会失效。
The Agent Architect
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