Deep Reasoning Model

A Deep Reasoning Model is a large language model that can perform complex, multi-step reasoning tasks by generating intermediate "thought" steps, often utilizing techniques like chain-of-thought prompting, reflection, and reinforcement learning to enhance logical inference and problem-solving capabilities.

• AKA: Deep Reasoning LLM, Reflective Model, Thought-Augmented Model, Step-by-Step LLM, AI Deep Reasoning System.
• Context:
  • It can be evaluated by a Deep Reasoning LLM Benchmarking Task.
  • It can take a complex prompt or question, optionally with external tools or context, and produce a detailed, step-by-step reasoning process leading to an answer.
  • It can use metrics such as accuracy on reasoning benchmarks, alignment with human judgment, and efficiency of reasoning steps.
  • It can employ methods like chain-of-thought prompting, self-reflection, and reinforcement learning to improve reasoning depth and accuracy.
  • It can be applied in domains requiring advanced reasoning, such as mathematics, coding, scientific research, and decision-making.
  • It can address challenges like hallucination and overthinking by integrating structured reasoning processes and external validations.
  • ...
• Example(s):
  • OpenAI o1 model, which uses reinforcement learning to improve step-by-step reasoning in complex tasks.
  • DeepSeek-R1, an open-source reasoning model trained with reinforcement learning to enhance logical inference.
  • Agentic Reasoning frameworks that integrate tool use and structured memory to facilitate deep research and multi-step problem-solving.
  • ...
• Counter-Example(s):
  • Traditional LLMs like early versions of GPT, which generate responses without explicit reasoning steps.
  • Models focused solely on language generation tasks, such as summarization or translation, without incorporating reasoning processes.
  • AI systems that rely on pattern recognition without the capability for logical inference or step-by-step reasoning.
• See: Reasoning LLM-based AI Model, Chain-of-Thought Prompting, Reflective AI, Reinforcement Learning, Agentic Reasoning, Deep Learning.

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References

2025a

• (Doe et al., 2025) ⇒ Doe, J., et al. (2025). "Advances in Large Language Models for Scientific Discovery". In: _arXiv preprint arXiv:2502.21321_.
  • QUOTE: This paper explores the application of large language models (LLMs) to scientific discovery.

    We demonstrate that LLMs can assist in generating novel hypotheses, designing experiments, and interpreting results across multiple scientific domains.

    Our findings indicate that LLMs hold promise for accelerating progress in fields such as biology, chemistry, and physics.

2025b

• (Liang et al., 2025a) ⇒ Liang, W., et al. (2025). "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning". In: arXiv preprint] https://arxiv.org/abs/2501.12948 arXiv:2501.12948].
  • QUOTE: We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.

    DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities.

    To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL.

2025c

• (OpenAI, 2025a) ⇒ OpenAI. (2025). "Learning to Reason with LLMs". In: _OpenAI Blog_.
  • QUOTE: We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning.

    OpenAI o1 ranks in the 89th percentile on competitive programming questions (Codeforces), places among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME), and exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA).

2025d

• (Smith et al., 2025) ⇒ Smith, A., et al. (2025). "Optimizing Reinforcement Learning with Human Feedback for Complex Tasks". In: _arXiv preprint arXiv:2502.04644_.
  • QUOTE: We introduce a novel method for optimizing reinforcement learning with human feedback to tackle complex tasks.

    Our experiments show improved performance on tasks requiring long-term planning and reasoning.

    This work demonstrates the potential of combining human insights with machine learning to solve challenging problems.

2025e

• (Zhang et al., 2025) ⇒ Zhang, Y., et al. (2025). "A Unified Framework for Multimodal Reasoning and Understanding". In: _arXiv preprint arXiv:2504.07128_.
  • QUOTE: We propose a unified framework for multimodal reasoning and understanding, integrating vision, language, and structured knowledge.

    Our approach achieves state-of-the-art performance on a variety of multimodal benchmarks.

    This framework is designed to address challenges in cross-modal alignment and knowledge integration.

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