arXiv:2606.03461v1 Announce Type: new Abstract: Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficulty, harness design, and student capacity. We investigate this pedagogical link using Terminal-Lego, a scalable pipeline that transforms multi-domain real-world issues into environment-verified agentic tasks. Surprisingly, standalone performance does not dictate teaching efficacy: while Claude Opus 4.6 achieves higher scores on Terminal-Bench 2.0, students fine-tuned on trajectories from DeepSeek-V3
This research provides a timely update on AI agent training, specifically disentangling agent performance from teaching efficacy in the fast-evolving field of autonomous systems.
A strategic reader should care because this challenges conventional assumptions about AI model superiority, suggesting that less performant models might be better teachers, impacting resource allocation and training methodologies.
The understanding that a 'stronger' agent isn't necessarily a 'better' teacher changes how developers might approach fine-tuning and curriculum design for terminal agents.
- · AI model developers
- · Organizations training autonomous agents
- · Less performant, but pedagogically effective, AI models
- · Organizations solely relying on standalone performance metrics for teacher selec
- · Developers of 'strong' models if their pedagogical efficacy is low
Further research will likely focus on identifying specific characteristics that make AI models effective teachers.
This could lead to a bifurcation in AI development, with some models optimized for task performance and others for pedagogical value.
The development of 'pedagogical AI' could become a new subfield, focusing on optimizing AI models to efficiently train other AI systems or even human users.
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Read at arXiv cs.AI