arXiv:2605.06597v2 Announce Type: replace-cross Abstract: Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains challenging because self-generated trajectories are free-form, correctness is task-dependent, and plausible rationales can still provide unstable or unreliable supervision. Existing methods mainly examine isolated design choices, leaving their effectiveness, roles, and interactions unclear. In this paper, we propose UniSD, a unified framework to systematically
The increasing scale and complexity of LLMs necessitate more efficient and reliable adaptation methods like self-distillation to overcome existing challenges.
This research provides a unified framework for improving the stability and effectiveness of self-distillation in large language models, potentially reducing reliance on external teachers and enhancing model performance.
Current fragmented approaches to LLM self-distillation may be replaced by more systematic and effective frameworks, leading to more robust and adaptable models.
- · AI researchers
- · LLM developers
- · Companies deploying custom LLMs
- · Companies relying solely on external teachers for LLM adaptation
More efficient and cost-effective finetuning and adaptation of large language models for various applications.
Reduced barriers to entry for organizations developing specialized LLMs, as fewer external computational resources may be needed for improvement.
Acceleration of AI agent development due to more reliable foundation models, potentially enabling more complex autonomous systems.
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Read at arXiv cs.LG