What is Missing from AI Post-Training AI: An Empirical Analysis
Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and improve downstream performance, raising the prospect of AI-for-AI. We argue that this picture conflates two distinct capabilities: execution-level capability, iterating within a selected training strategy; and strategy-level capability, revising the high-level judgment as experimental evidence accumulates. Analyzing a large corpus of publicly released post-training trajectories, we find that across different tasks, the agent's training strategy is locked in at
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- LinkedLinked via arxiv author · 85%Joy Jia Yin Lim →
“What is Missing from AI Post-Training AI: An Empirical Analysis”
- LinkedLinked via arxiv author · 85%Jinxin Huang →
“What is Missing from AI Post-Training AI: An Empirical Analysis”