Mini-R1: Reproduce Deepseek R1 „aha moment“ a RL tutorial

Open-R1: a fully open reproduction of DeepSeek-R1

From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates

Understanding How DeepSeek's Flagship Open-Weight Models Evolved

One Year Since the “DeepSeek Moment”

Deep R Programming

arXiv:2301. 01188v5 Announce Type: replace-cross Abstract: Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science.

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design

The paper examines how evaluation design can cause significant fluctuations in the benchmark results of reasoning models, particularly the Deepseek‑R1‑Distill series. It shows that subtle changes in evaluation conditions lead to large variations in reported performance, a phenomenon also seen in other open‑source models fine‑tuned from Deepseek‑R1‑Distill and in the QwQ‑32B model. The authors call for a more rigorous evaluation paradigm and provide empirical assessments of the Deepseek‑R1‑Distill models.

The State Of LLMs 2025: Progress, Problems, and Predictions

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.