Phi-4-reasoning Technical Report
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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Marah Abdin
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2025-05-05T07:55:09.552Z
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Marah Abdin
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Sahaj Agarwal
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2025-05-05T07:55:27.348Z
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Sahaj Agarwal
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Ahmed Awadallah
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2025-05-05T07:55:47.739Z
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Ahmed Awadallah
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Vidhisha Balachandran
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Vidhisha Balachandran
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Harkirat Behl
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2025-05-05T07:56:12.664Z
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Harkirat Behl
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Lingjiao Chen
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Gustavo de Rosa
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2025-05-02T06:35:09.698Z
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Gustavo de Rosa
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Suriya Gunasekar
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2025-05-05T07:56:25.927Z
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Gunasekar
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Mojan Javaheripi
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2025-05-05T07:56:31.792Z
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Mojan Javaheripi
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Neel Joshi
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Piero Kauffmann
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Piero Kauffmann
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Yash Lara
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Yash Lara
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Caio César Teodoro Mendes
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Arindam Mitra
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Besmira Nushi
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2025-05-05T07:57:12.615Z
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Besmira Nushi
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Dimitris Papailiopoulos
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2025-05-05T07:57:19.858Z
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Dimitris Papailiopoulos
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Olli Saarikivi
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2025-05-05T07:57:26.121Z
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Olli Saarikivi
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Shital Shah
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Vaishnavi Shrivastava
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2025-05-05T07:57:36.639Z
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Vaishnavi Shrivastava
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Vibhav Vineet
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Vibhav Vineet
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Yue Wu
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Safoora Yousefi
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2025-05-05T07:57:49.586Z
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Safoora Yousefi
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Guoqing Zheng
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2025-05-05T07:57:55.516Z
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Guoqing Zheng
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Phi-4-reasoning Technical Report
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.
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.
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