What I’ve been building: ATOM Report, post-training course, finishing my book, and ongoing research

2026-04-14 · Source: Interconnects AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

The ATOM Report, an updated technical analysis released in April 2026, details the open language model ecosystem, building on the August 2025 ATOM Project manifesto advocating for U.S. investment in open models. This report, available on arXiv (2604.07190), tracks GPT-OSS's growth, inference market share, and the influence of Chinese mid-tier players like Moonshot, Z.ai, and MiniMax. It introduces updates to the Relative Adoption Metric (RAM), a time-varying, size-normalized score where a value >1 indicates a model is on track to be a top 10 most downloaded model in its category. The report also analyzes the early adoption of Gemma 4. Additionally, a new book, "The RLHF Book," is available for pre-order, focusing on post-training language models, accompanied by a free YouTube lecture series and codebases. Recent technical research includes papers on multi-turn language model capabilities (TurnWise, 2603.16759) and meta-reinforcement learning with self-reflection for agentic search (2603.11327).

Key takeaway

For AI architects and data scientists evaluating open-source LLMs, the ATOM Report provides critical metrics like the Relative Adoption Metric (RAM) to assess model traction and market influence, particularly from Chinese developers. Your teams should consider integrating these adoption insights when selecting foundational models. Additionally, explore the "RLHF Book" and its accompanying course to deepen your understanding of post-training techniques, which are essential for optimizing model performance and developing advanced agentic systems.

Key insights

The open language model ecosystem is rapidly evolving, driven by new models and advanced post-training techniques.

Principles

• Open models require continuous ecosystem tracking.
• Multi-turn interactions are crucial for agentic UIs.

Method

The Relative Adoption Metric (RAM) evaluates model adoption by normalizing for size and time, with scores >1 indicating top-tier potential.

In practice

• Pre-order "The RLHF Book" for post-training insights.
• Explore the free RLHF book course on YouTube.
• Review arXiv papers 2603.16759 and 2603.11327.

Topics

• Open Language Model Ecosystem
• ATOM Report
• Relative Adoption Metric
• RLHF Book
• Post-training LLMs

Best for: CTO, VP of Engineering/Data, AI Architect, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Interconnects AI.