arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales, hallucinated supervision, and static teacher-student interactions. Existing reasoning distillation methods, including mentor-based approaches, predominantly operate in an open-loop manner, implicitly assuming uniform teacher reliability and consequently propagating erroneous intermediate reasoning. We propose GateKD, a confidence-gated closed-loop distillation framework that enables robust reason
The increasing complexity and deployment of LLMs necessitate more robust and efficient methods for distilling their capabilities into smaller models without sacrificing reasoning quality.
Improving distillation techniques for multi-step reasoning directly impacts the performance, cost, and accessibility of advanced AI systems, expanding their deployability across various applications.
The introduction of confidence-gated, closed-loop distillation can significantly reduce errors from noisy rationales and hallucinated supervision found in existing open-loop methods.
- · AI developers
- · On-device AI applications
- · AI infrastructure providers
- · Edge computing
- · Companies reliant on large, inefficient LLMs
- · Providers of less robust distillation methods
More accurate and efficient smaller language models become available for practical use.
Broader adoption of sophisticated AI reasoning in cost-sensitive and resource-constrained environments increases.
This could accelerate the development of autonomous AI agents benefiting from more reliable micro-models, decentralizing high-end AI capabilities.
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Read at arXiv cs.CL