DASD-4B: Better Long-CoT Reasoning for Small LLMs
Автор: AI Research Roundup
Загружено: 2026-01-15
Просмотров: 27
In this AI Research Roundup episode, Alex discusses the paper: 'Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning' The researchers introduce DASD-4B-Thinking, an open-source model that sets new performance standards for its size using a novel distillation method. They address limitations in current long Chain-of-Thought training by improving teacher-student alignment and reducing exposure bias. The approach uses a two-stage curriculum that transitions from low-temperature samples to high-temperature distributional diversity. Additionally, the framework employs Divergence-aware Sampling to identify and learn from specific patterns where the teacher and student models differ. This methodology enables a lightweight model to achieve superior reasoning capabilities through more effective knowledge transfer. Paper URL: https://arxiv.org/abs/2601.09088 #AI #MachineLearning #DeepLearning #LLM #Reasoning #Distillation #ChainOfThought #NLP
Resources:
GitHub: https://github.com/D2I-ai/dasd-thinking
Hugging Face model: https://huggingface.co/Alibaba-Apsara...
Hugging Face model 2: https://huggingface.co/Alibaba-Apsara...
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