New strategies for data, objectives, and supervision in LLM post-training

This cluster explores emerging methods to make large language model training more data-efficient, targeted, and robust. Work spans synthetic domain-specific instruction generation, adversarial co-evolution of code models with their test suites, and EBFT-style fine-tuning via feature matching instead of just token-level loss. Other pieces compare supervised fine-tuning with reinforcement learning approaches, propose soft-prompted semantic normalization for unsupervised structuring of domains like research abstracts, and introduce Bayesian teaching to optimally select training examples. Together, they point toward smarter curricula and data-centric techniques that can deliver stronger models without brute-force scaling of data and compute.