title: Phi-4-reasoning Technical Report
summary: We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that
  achieves strong performance on complex reasoning tasks. Trained via supervised
  fine-tuning of Phi-4 on carefully curated set of "teachable" prompts-selected
  for the right level of complexity and diversity-and reasoning demonstrations
  generated using o3-mini, Phi-4-reasoning generates detailed reasoning chains
  that effectively leverage inference-time compute. We further develop
  Phi-4-reasoning-plus, a variant enhanced through a short phase of outcome-based
  reinforcement learning that offers higher performance by generating longer
  reasoning traces. Across a wide range of reasoning tasks, both models
  outperform significantly larger open-weight models such as
  DeepSeek-R1-Distill-Llama-70B model and approach the performance levels of full
  DeepSeek-R1 model. Our comprehensive evaluations span benchmarks in math and
  scientific reasoning, coding, algorithmic problem solving, planning, and
  spatial understanding. Interestingly, we observe a non-trivial transfer of
  improvements to general-purpose benchmarks as well. In this report, we provide
  insights into our training data, our training methodologies, and our
  evaluations. We show that the benefit of careful data curation for supervised
  fine-tuning (SFT) extends to reasoning language models, and can be further
  amplified by reinforcement learning (RL). Finally, our evaluation points to
  opportunities for improving how we assess the performance and robustness of
  reasoning models.
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paper_title: Phi-4-reasoning Technical Report
paper_summary: We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that
  achieves strong performance on complex reasoning tasks. Trained via supervised
  fine-tuning of Phi-4 on carefully curated set of "teachable" prompts-selected
  for the right level of complexity and diversity-and reasoning demonstrations
  generated using o3-mini, Phi-4-reasoning generates detailed reasoning chains
  that effectively leverage inference-time compute. We further develop
  Phi-4-reasoning-plus, a variant enhanced through a short phase of outcome-based
  reinforcement learning that offers higher performance by generating longer
  reasoning traces. Across a wide range of reasoning tasks, both models
  outperform significantly larger open-weight models such as
  DeepSeek-R1-Distill-Llama-70B model and approach the performance levels of full
  DeepSeek-R1 model. Our comprehensive evaluations span benchmarks in math and
  scientific reasoning, coding, algorithmic problem solving, planning, and
  spatial understanding. Interestingly, we observe a non-trivial transfer of
  improvements to general-purpose benchmarks as well. In this report, we provide
  insights into our training data, our training methodologies, and our
  evaluations. We show that the benefit of careful data curation for supervised
  fine-tuning (SFT) extends to reasoning language models, and can be further
  amplified by reinforcement learning (RL). Finally, our evaluation points to
  opportunities for improving how we assess the performance and robustness of
  reasoning models.
paper_ai_summary: Phi-4-reasoning, a 14-billion parameter model enhanced with supervised fine-tuning and reinforcement learning, outperforms larger models on complex reasoning tasks across various benchmarks.
paper_ai_keywords: Phi-4-reasoning Phi-4-reasoning-plus supervised fine-tuning "teachable" prompts reasoning demonstrations o3-mini outcome-based reinforcement learning DeepSeek-R1-Distill-Llama-70B DeepSeek-R1 math reasoning scientific reasoning coding algorithmic problem solving planning spatial understanding general-purpose benchmarks data curation performance assessment