mlsanityData to 14 Oct 2026

Brief analysis of DeepSeek R1 and its implications for Generative AI

arXiv 2502.02523 ↗First posted 4 Feb 2025cs.LGcited by 10

On the map: LLM reasoning → Reasoning benchmarks and evaluation

also in 1 more
Abstract (arXiv)

In late January 2025, DeepSeek released their new reasoning model (DeepSeek R1); which was developed at a fraction of the cost yet remains competitive with OpenAI's models, despite the US's GPU export ban. This report discusses the model, and what its release means for the field of Generative AI more widely. We briefly discuss other models released from China in recent weeks, their similarities; innovative use of Mixture of Experts (MoE), Reinforcement Learning (RL) and clever engineering appear to be key factors in the capabilities of these models. This think piece has been written to a tight timescale, providing broad coverage of the topic, and serves as introductory material for those looking to understand the model's technical advancements, as well as its place in the ecosystem. Several further areas of research are identified.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 7 of its 43 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

7 of 43 references.

References · 7

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2024Mixtral of Expertscited by 1,823
  3. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
  4. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  5. 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 313
  6. 2024Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflectioncited by 18
  7. 2024LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashingcited by 5

Built on by

Cited by year

Citing papers per year: 2025: 10.

10 papers.

  1. 2025Agentic Large Language Models, a surveycited by 108
  2. 2025Improving RL Exploration for LLM Reasoning through Retrospective Replaycited by 19
  3. 2025Pre-Trained Policy Discriminators are General Reward Modelscited by 15
  4. 2025RLSR: Reinforcement Learning from Self Rewardcited by 14
  5. 2025Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospectscited by 9
  6. 2025Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approachcited by 8
  7. 2025An Interpretable Automated Mechanism Design Framework with Large Language Modelscited by 2
  8. 2025Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1cited by 0
  9. 2025Assessment of Evolving Large Language Models in Upper Secondary Mathematicscited by 0
  10. 2025A comparison of pipelines for the translation of a low resource language based on transformerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models
  2. 2025A Review of DeepSeek Models' Key Innovative Techniques
  3. 2025DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models
  4. 2025DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
  5. 2025Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies
  6. 2025Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis
  7. 2025Understanding R1-Zero-Like Training: A Critical Perspective
  8. 2023An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications