arXiv:2606.26091v1 Announce Type: new Abstract: On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a correct demonstration to provide dense token-level feedback. We show that this could come at a hidden cost: rollout diversity decreases and pass@k curves flatten (i.e., generating more rollouts fails to improve accuracy). We trace this to compounding biases in the design of self-distillation with sampled demonstrations. The teacher scores each student rollout while conditioned on a sampled correct roll
This research provides a timely analysis as large language model development increasingly relies on self-distillation and other complex training techniques aiming for higher performance.
A strategic reader should care because this highlights a potential trade-off between immediate accuracy gains and the robustness or diversity of AI model outputs, impacting future AI development and deployment strategies.
This research suggests a hidden cost in current self-distillation methods, indicating that pursuit of high pass@1 accuracy might inadvertently limit the breadth of useful outputs from AI models, thus requiring re-evaluation of training methodologies.
- · AI researchers focusing on diversity and robustness
- · Developers implementing quality control for AI-generated content
- · AI models relying solely on current self-distillation techniques for high pass@1
- · Applications requiring diverse and novel AI outputs
Self-distillation methods may need significant revisions to balance accuracy with output diversity.
The industry may shift towards more complex training regimes that explicitly optimize for both performance metrics and diversity.
Future AI systems could exhibit improved generalization and adaptability due to more robust training techniques that address these biases.
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Read at arXiv cs.LG