arXiv:2605.30651v1 Announce Type: new Abstract: We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods rely on heuristics such as trajectory quality or model confidence, but they often overlook whether a trajectory is learnable by the student. In this paper, we present LARK, a learnability-grounded method for reasoning trajectory selection. LARK selects trajectories that the student can learn efficiently while preserving the generalization of the full training distributio
The proliferation of powerful large language models necessitates more efficient and effective methods for distilling knowledge and capabilities, driving innovation in learning processes.
Improving the efficiency of reasoning distillation directly impacts the cost and speed of developing advanced AI systems and agents, democratizing access to complex AI capabilities.
The focus for AI training shifts from solely 'teacher quality' to 'student learnability,' emphasizing adaptive and personalized learning processes for AI models.
- · AI developers
- · Companies with limited compute
- · Researchers in AI training optimization
- · Startups developing more efficient AI models
- · Inefficient AI training methodologies
- · Models reliant on brute-force data scaling
More efficient training processes will lead to the faster development and deployment of more capable AI models.
Reduced compute requirements for advanced models could broaden access to cutting-edge AI, fostering innovation beyond well-funded labs.
A potential increase in the speed of AI progress could accelerate the development of autonomous AI agents, impacting various industries and human-computer interaction paradigms.
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