arXiv AI
How AI Assistance Affects Human Skill Development: A Study of Learning with Logic Puzzles
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AI Assistance Reduces Persistence and Hurts Independent Performance
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arXiv Machine Learning
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Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models
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AI Assistants Overassist
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arXiv AI
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Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics
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LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations
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arXiv AI
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What is Missing from AI Post-Training AI: An Empirical Analysis
The paper investigates the limitations of post-training AI agents that can autonomously train large language models. It distinguishes between execution-level capability—making adjustments within a chosen training strategy—and strategy-level capability—revising the overall approach based on new evidence. Analysis of many public post-training runs shows that agents lock into a strategy early and then only perform local tweaks, regardless of task. Experiments with experience scaffolds, human guidance, and extra compute improve execution but do not enable strategy reevaluation, indicating that agents lack a mechanism to spontaneously reassess their strategy during training.