Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution

8 Oct 2026Reported by arXiv21

Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution. It centres on Inference, and also names Qwen. Reported by arXiv. Bharat Hunt files it under AI Models and AI Regulation — the section covering a new or updated model, its capabilities, benchmarks or availability.

Written by Bharat Hunt from the headline and the coverage below. The original reporting is the source of truth.

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Reported by arXiv

First seen 8 oct 2026

arXivWhich Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution8 Oct 2026Yuhan Liu

AI Models23

Selective Transfer of RL Updates for Visual Reasoning

Selective Transfer of RL Updates for Visual Reasoning. It centres on Benchmarks, and also names Qwen. Reported by arXiv. Bharat Hunt files it under AI Models and Computer Vision — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv6 Oct 2026

Robotics23

LLMs are General Asynchronous Agents

LLMs are General Asynchronous Agents. It centres on Inference, and also names Qwen. Reported by arXiv. Bharat Hunt files it under Robotics and AI Tools — the section covering embodied AI, humanoids, autonomous vehicles and drones. Open the original report for the full detail.

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Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution

Companies, models and people in this story

Reported by arXiv

Selective Transfer of RL Updates for Visual Reasoning

LLMs are General Asynchronous Agents

Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency

SemanticFold: Latent Sequence Compression SeparatesLanguage Modeling, Decodability, and Reasoning

Companies, models and people in this story

Reported by arXiv

Related AI coverage

Selective Transfer of RL Updates for Visual Reasoning

LLMs are General Asynchronous Agents

Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency

SemanticFold: Latent Sequence Compression SeparatesLanguage Modeling, Decodability, and Reasoning