According to a paper published on arXiv.org, post-training processes make large language models less human-like, though the specific findings and methodology were not detailed in the available excerpt. The research, authored by Marcel Binz, Elif Akata, and a team of collaborators, was published on May 27, 2026.
Separately, researchers are examining alignment tuning through a data-centric lens. According to another arXiv.org paper accepted at the Findings of ACL 2026, alignment tuning can be reframed as a pipeline design problem. The survey decomposes alignment data construction into three stages: response synthesis, preference evaluation, and preference instantiation. The authors note that “much of the alignment tuning literature is organized around optimization objectives, while the construction of alignment data is often treated implicitly.”
Additionally, research on on-policy distillation identifies an “Off-policy Teacher Decay” problem, according to a third arXiv.org paper. The researchers propose Early Stopping Rollout (ESR), which restricts rollout generation to the first response tokens. According to the paper, ESR “surpasses the full rollout OPD performance across model size, family, tasks and training regime” while exhibiting higher GPU efficiency and training stability.