Technical report reveals trends in open language models; researcher completes post-training book and contributes to recent research

A researcher specializing in language model post-training has revealed an array of ongoing projects. Central to this work is the ATOM Report, a detailed technical analysis of the open language model ecosystem featuring current data on model adoption patterns, the rising prominence of Chinese companies like Moonshot and MiniMax, and recent U.S. developments in open models. The report introduces the Relative Adoption Metric (RAM), a tool designed to assess model adoption while accounting for temporal changes and model size differences. This single-number metric becomes interpretable: scores above 1 suggest a model is progressing toward the ten most-downloaded models within its size tier. The analysis includes examination of the Gemma 4 release, which demonstrated impressive adoption metrics. Concurrently, the researcher finished "RLHF Book," a comprehensive guide intended to bridge beginners and experts in post-training techniques. The book will be accompanied by free video lectures published on YouTube, extending its impact beyond traditional publishing. The researcher also participated in two recent papers. One investigates performance divergences between single-turn and multi-turn dialogue contexts across different models, while the other explores meta-learning approaches within reinforcement learning for agent-based search, analyzing how models can leverage prior attempts to enhance subsequent results.