Sebastian Raschka has published his curated LLM research paper list for the first half of 2025, organized by topic for the first time. The list covers 7 categories: Reasoning Models (split into training, inference-time strategies, and evaluation), Reinforcement Learning for LLMs, Inference-Time Scaling, Efficient Training and Architectures, Diffusion-Based Language Models, Multimodal and Vision-Language Models, and Data and Pre-training Datasets. Reasoning dominates the list, reflecting where the field spent its energy from January through June.
The structural change matters. Last year's chronological format buried connections between related work. Grouping by topic makes it possible to trace how, for example, reinforcement learning with verifiable rewards became the dominant training strategy for reasoning models, a thread Raschka covers in a companion article on RL for LLM reasoning. The original article also includes direct links to each paper, making this a functional reading queue, not just a reference list.
Raschka plans bi-yearly updates going forward to keep the lists usable rather than exhaustive. Deeper topic-specific writeups on the most impactful papers are coming in future issues. If you work in LLM research or applied AI, the full list is worth opening: the category breakdowns alone reveal which problems the field treated as solved and which it is still circling.
[READ ORIGINAL →]