When Does Online Imitation Learning Help in LLM Post-Training? The Role of (Non-)Realizability Beyond Horizon
arXiv:2606.30445v1 Announce Type: new Abstract: Online imitation learning (IL), particularly on-policy distillation, has emerged as a strong LLM post-training approach, often outperforming offline supervised fine-tuning (SFT). Yet a principled understanding of when and why online interaction helps remains unclear. In this work, we challenge the view that error accumulation is the main source of online IL's advantage, and instead show that the benefits of online interaction depend critically on whether the setting is realizable, i.e., whether the student policy class can represent the expert po
This paper offers a foundational theoretical understanding of online imitation learning in LLMs, which is a rapidly evolving area of research for improving model performance beyond supervised fine-tuning.
Understanding the conditions under which online imitation learning genuinely improves LLMs helps refine post-training methodologies, leading to more efficient and effective development of advanced AI models.
The theoretical framework presented challenges previous assumptions about error accumulation in online imitation learning, redirecting research focus towards the 'realizability' factor for optimization.
- · AI researchers
- · LLM developers
- · Companies investing in advanced AI training platforms
- · Researchers relying solely on error accumulation hypotheses for online IL
More targeted and efficient online imitation learning techniques emerge for LLMs.
Improved LLM performance leads to more robust and capable AI agents.
Accelerated development of artificial general intelligence (AGI) as LLM capabilities advance through optimized training.
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Read at arXiv cs.LG