LLM Research Papers: 200+ papers Jan–Jun 2025, topic list

Sebastian Raschka published a topic-organized collection of more than 200 LLM research papers covering January to June 2025, posted Jul 01, 2025. The compilation groups entries by technical theme and is presented as a bi-yearly update to keep the list digestible and timely.

What the list contains

The collection is organized into topic sections rather than date order. Major categories are Reasoning Models (subdivided into Training Reasoning Models, Inference-Time Reasoning Strategies, and 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, and Data & Pre-training Datasets.

The Reasoning Models section alone contains dozens of papers dated across the first half of 2025. Early-January entries include 8 Jan, "Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought". Later items show the diversity of approaches: 22 Jan, "Kimi k1.5: Scaling Reinforcement Learning with LLMs"; 31 Mar, "Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model"; 9 Jun, "Reinforcement Pre-Training"; and 26 Jun, "Bridging Offline and Online Reinforcement Learning for LLMs".

Raschka flags subtopics within reasoning: training strategies intended to improve reasoning ability, inference-time methods that boost reasoning without retraining, and broader evaluation or understanding of reasoning. The Training Reasoning Models subsection explicitly lists numerous reinforcement-learning-centered works and related methods dated between January and June 2025.

The post also notes a practical resource alongside the list. Raschka made all 30 chapters of his Machine Learning Q and AI book freely available for the summer, framing the list as summer reading and study material for interns and candidates preparing for interviews.

Why the list is reasoning-heavy

Raschka writes, "This year, my list is very reasoning model-heavy." Within the Training Reasoning Models subsection he observes that "much of the recent progress has centered around reinforcement learning (with verifiable rewards)," and he groups many papers that apply reinforcement learning to induce reasoning improvements in LLMs. The entries reflect both algorithmic experiments, for example "Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning" (20 Feb), and scaling-oriented efforts, for example "R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning" (10 Mar).

The list mixes approaches that target base-model training, such as "Reinforcement Pre-Training" (9 Jun) and hybrid training recipes like "Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging - An Open Recipe" (13 Feb), with inference-time strategies that trade compute for improved outputs. The Inference-Time Reasoning Strategies subsection is described as covering methods that improve reasoning dynamically at test time, often trading computational performance for modeling performance.

Why it matters

A curated, topic-organized index of 200+ papers lowers the discovery cost for researchers and practitioners tracking rapid progress. By highlighting the reinforcement-learning thread and separating training versus test-time strategies, the list makes it easier to spot clusters of work that share evaluation signals and training assumptions. Raschkas decision to publish the list bi-yearly also signals that the pace of LLM research remains high enough to require more frequent sampling than an annual roundup.

What to watch

Watch for Raschkas promised follow-up write-ups: he said he plans to revisit and discuss select papers in larger, topic-specific articles. Also monitor papers he highlighted in the reasoning subsection for subsequent code releases or broader adoption, for example entries such as "Reinforcement Pre-Training" (9 Jun) and "Open-Reasoner-Zero" (31 Mar), which indicate active experimentation with reinforcement learning at both open and proprietary fronts.