SelSkill applies dual-granularity preference learning to selective skill-or-skip decisions, improving task success by 10.9 points and execution precision by 29.1 points on ALFWorld with Qwen3-8B.
Alignment for Efficient Tool Calling of Large Language Models , booktitle =
2 Pith papers cite this work. Polarity classification is still indexing.
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DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
citing papers explorer
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Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
SelSkill applies dual-granularity preference learning to selective skill-or-skip decisions, improving task success by 10.9 points and execution precision by 29.1 points on ALFWorld with Qwen3-8B.
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DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.