ELLIS Units Paris & Oxford: NeurIPS 2026 Workshop - Foundations of LLM Post-Training in Changing Environments

Large language models (LLMs) are routinely adapted to downstream applications through post-training methods, such as instruction tuning and domain adaptation. Yet in real-world deployment, downstream tasks rarely remain fixed: objectives shift, data distributions drift, feedback signals evolve, and evaluation standards change over time. Post-training therefore becomes a process of repeated adaptation in non-stationary environments.

Despite its central role in modern foundation models, the theoretical foundations of this adaptive post-training paradigm remain limited. Current practices are largely heuristic, with incomplete understanding of statistical identifiability, optimization dynamics, robustness to misspecification, and trade-offs between adaptation and capability preservation. These gaps are particularly consequential in safety-critical settings, where unintended regressions or feedback loops may arise under evolving conditions.

This workshop will develop principled foundations for LLM post-training under task evolution. It will bring together researchers from machine learning theory, reinforcement learning, and AI safety to develop principled foundations for this.


Registration

For more information and to register for this paid event, please visit: https://www.fllmpt-work.shop/

Abstract deadline is 23 August 2026.