LLM Research Papers: The 2025 List (January to June)

LLM Research Papers: The 2025 List (January to June) A topic-organized collection of 200+ LLM research papers from 2025 As some of you know, I keep a running list of research papers I (want to) read and reference. About six months ago, I shared my 2024 list, which many readers found useful. So, I was thinking about doing this again. However, this time, I am incorporating that one piece of feedback kept coming up: "Can you organize the papers by topic instead of date?" The categories I came up with are: Reasoning Models - 1a. Training Reasoning Models - 1b. Inference-Time Reasoning Strategies - 1c. Evaluating LLMs and/or Understanding Reasoning Other Reinforcement Learning Methods for LLMs Other Inference-Time Scaling Methods Efficient Training & Architectures Diffusion-Based Language Models Multimodal & Vision-Language Models Data & Pre-training Datasets Also, as LLM research continues to be shared at a rapid pace, I have decided to break the list into bi-yearly updates. This way, the list stays digestible, timely, and hopefully useful for anyone looking for solid summer reading material. Please note that this is just a curated list for now. In future articles, I plan to revisit and discuss some of the more interesting or impactful papers in larger topic-specific write-ups. Stay tuned! Announcement: It's summer! And that means internship season, tech interviews, and lots of learning. To support those brushing up on intermediate to advanced machine learning and AI topics, I have made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: 🔗 https://sebastianraschka.com/books/ml-q-and-ai/#table-of-contents Whether you are just curious and want to learn something new or prepping for interviews, hopefully this comes in handy. Happy reading, and best of luck if you are interviewing! 1. Reasoning Models This year, my list is very reasoning model-heavy. So, I decided to subdivide it into 3 categories: Training, inference-time scaling, and more general understanding/evaluation. 1a. Training Reasoning Models This subsection focuses on training strategies specifically designed to improve reasoning abilities in LLMs. As you may see, much of the recent progress has centered around reinforcement learning (with verifiable rewards), which I covered in more detail in a previous article. 8 Jan, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682 13 Jan, The Lessons of Developing Process Reward Models in Mathematical Reasoning, https://arxiv.org/abs/2501.07301 16 Jan, Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models, https://arxiv.org/abs/2501.09686 20 Jan, Reasoning Language Models: A Blueprint, https://arxiv.org/abs/2501.11223 22 Jan, Kimi k1.5: Scaling Reinforcement Learning with LLMs, https://arxiv.org/abs//2501.12599 22 Jan, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, https://arxiv.org/abs/2501.12948 3 Feb, Competitive Programming with Large Reasoning Models, https://arxiv.org/abs/2502.06807 5 Feb, Demystifying Long Chain-of-Thought Reasoning in LLMs, Demystifying Long Chain-of-Thought Reasoning in LLMs, https://arxiv.org/abs/2502.03373 5 Feb, LIMO: Less is More for Reasoning, https://arxiv.org/abs/2502.03387 5 Feb, Teaching Language Models to Critique via Reinforcement Learning, https://arxiv.org/abs/2502.03492 6 Feb, Training Language Models to Reason Efficiently, https://arxiv.org/abs/2502.04463 10 Feb, Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning,…