LLM Research Papers: A Curated List for January to June 2025

Summary

Sebastian Raschka presents a curated reading list of large language model research papers published or shared from January through June 2025. Unlike his earlier date-oriented list, this collection is organized by topic so readers can browse related work together. The categories include reasoning-model training, inference-time reasoning strategies, LLM evaluation and reasoning analysis, other reinforcement-learning methods, inference-time scaling, efficient training and architectures, diffusion-based language models, multimodal and vision-language models, and data and pre-training datasets. Reasoning models receive the most attention and are divided into training, test-time scaling, and broader understanding or evaluation; the training section particularly reflects the period’s focus on reinforcement learning with verifiable rewards. The list includes papers on models and methods such as DeepSeek-R1, Kimi k1.5, Qwen3, Llama-Nemotron, AlphaEvolve, search-enabled reasoning, rule-based rewards, and smaller reasoning models, but the article presents them as references rather than detailed evaluations. Raschka plans to publish more substantial topic-specific discussions of selected papers later. He also announces that all 30 chapters of his Machine Learning Q and AI book will be freely available during the summer for learning and interview preparation.