Are recent LLM gains mostly from pretraining or post-training?

Reddit r/ArtificialInteligence 06/28/26, 01:05 PM News

llm pretraining post-training frontier-models reinforcement-learning fine-tuning

Summary

A discussion question exploring whether recent LLM gains are driven more by pretraining or post-training techniques like RL and fine-tuning, given that both require significant compute.

from what i've read, recent frontier llms seem to use broadly similar transformer architectures, while many of the visible improvements (reasoning, coding, and agentic behavior) appear to come from post-training techniques such as supervised fine-tuning, RL, preference optimization, and tool-use training. at the same time, labs continue to spend enormous compute on pretraining with larger, higher-quality datasets, so i assume pretraining is still doing most of the heavy lifting. is there any research, ablation study, or industry experience that sheds light on how much each stage contributes to recent capability gains? is there a growing consensus that post-training is now the main differentiator between frontier models, or is pretraining still responsible for most of the improvements?

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