CoRD: Multi-Teacher Distillation for Long-CoT

In this AI Research Roundup episode, Alex discusses the paper: 'Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding'. The authors introduce CoRD, a framework designed to distill Long Chain-of-Thought capabilities from large models into smaller, more efficient LLMs. Unlike traditional post-hoc curation, CoRD uses a step-wise approach where multiple teacher models collaborate to construct reasoning trajectories. It employs prompt-guided segmentation with specific markers like the think Step marker to ensure consistency and a perplexity-based selection method to evaluate reasoning steps. This allows the system to navigate the complex search space of long-form reasoning more effectively than existing methods. Paper URL: https://arxiv.org/abs/2605.02290 #AI #MachineLearning #DeepLearning #LLM #ChainOfThought #Distillation #CoRD #ReasoningModels

Resources:
- GitHub: https://github.com/DISL-Lab/CoRD

Видео CoRD: Multi-Teacher Distillation for Long-CoT канала AI Research Roundup