ICLR 2026 Workshop on Scaling Post-training for LLMs

About the Workshop

Post-training—spanning Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and beyond is no longer a final adaptation step. It has become a compute-intensive, first-class phase that increasingly determines the capabilities, safety, and efficiency of large language models. Yet, despite its growing importance, post-training at scale remains poorly understood.

Recent frontier models allocate a substantial and rapidly growing fraction of total compute to post-training, while academic efforts are only beginning to develop principled, scalable methodologies. Unlike pre-training, where scaling laws and design trade-offs are well studied, post-training lacks a comparable scientific framework.

The Workshop on Scaling Post-Training for LLMs (SPOT) aims to address this gap by focusing on the foundational principles that make post-training scale, across algorithms, data, architectures, systems, and objectives. Rather than targeting specific applications, SPOT centers on understanding the design choices, bottlenecks, and trade-offs that govern efficiency, stability, and asymptotic performance in large-scale post-training.

Topics of interest include:

• Algorithms: SFT and RL formulations, reward modeling, and objectives that enable stable and compute-efficient scaling.
• Systems: Infrastructure challenges unique to post-training, including rollout latency, training–inference mismatches, distributed execution, and KV-cache management.
• Architectures: The impact of design choices such as MoE, sparse attention, and model specialization on sample efficiency and generalization.
• Data: The role of data curation and synthetic data in scaling post-training and shaping generalization.
• Environments & Safety: Post-training in multimodal, embodied, and real-world settings, with emphasis on evaluation fidelity and safety.
• Feedback & Verification: Efficient, dense feedback mechanisms as evaluation costs increasingly rival training costs.

SPOT brings together academic and industrial researchers to share experiences, identify open challenges, and move toward a principled science of post-training at scale.

Call for Papers

To be announced.