> [!Warning] > > > 🚨 NFT-7B is specifically designed for mathematical reasoning tasks. We do not recommend using this model for general conversation or non-mathematical tasks. > > Huayu Chen, Kaiwen Zheng, Qinsheng Zhang, Ganqu Cui, Yin Cui, Haotian Ye, Tsung-Yi Lin, Ming-Yu Liu, Jun Zhu, Haoxiang Wang [Paper] | [Blog] | [Code] | [Dataset] | [Models] | [Citation] NFT-7B is a math reasoning model finetuned from Qwen2.5-Math-7B using the Negative-aware Fine-Tuning (NFT) algorithm. NFT is a supervised learning approach that enables LLMs to reflect on their failures and improve autonomously with no external teachers. Unlike traditional supervised methods that discard incorrect answers, NFT constructs an implicit negative policy to model and learn from these failures, achieving performance comparable to leading RL algorithms like GRPO and DAPO. This model demonstrates that self-reflective improvement is not exclusive to RL methods—supervised learning can effectively leverage negative feedback for continuous improvement in mathematical reasoning tasks. This model is released under the NVIDIA Non-Commercial License. The model is for research and development only. — NFT-7B:…
Модальности:
Генерация текста
Области применения:
Математика Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: nvidia
Теги: qwen2, nvidia, math, reasoning, post-training, qwen, conversational, en
Лайков: 3 | Загрузок: 190
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
