Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
Summary
CoRD is a collaborative multi-teacher decoding framework that synthesizes reasoning trajectories through predictive perplexity scoring and beam search, enabling efficient distillation of large reasoning models with high-quality outputs and generalized performance.
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Paper page - Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
Source: https://huggingface.co/papers/2605.02290
Abstract
CoRD is a collaborative multi-teacher decoding framework that synthesizes reasoning trajectories through predictive perplexity scoring and beam search, enabling efficient distillation of large reasoning models with high-quality outputs and generalized performance.
Distilling large reasoning modelsis essential for makingLong-CoT reasoningpractical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overlooking collaboration amongheterogeneous teachersand lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We introduce CoRD, acollaborative multi-teacher decodingframework that performs step-wise reasoning synthesis guided bypredictive perplexity-based scoringandbeam search. This enables heterogeneous LRMs to jointly construct coherentreasoning trajectorieswhile efficiently preserving diverse, high-potential hypotheses. Experiments show that CoRD produces higher-quality reasoning data and achieves near teacher-level student performance with fewer,structured supervision signals, without substantial efficiency overhead. CoRD further generalizes well to out-of-domain and open-ended settings. The dataset and model are available at https://github.com/DISL-Lab/CoRD{https://github.com/DISL-Lab/CoRD}.
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