Hugging Face Trending Papers October 22, 2026

Training Small Language Models for Transferable Tool Selection

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The paper presents a method for training small language models to select appropriate tools for complex, multi-step user instructions, addressing the challenge of handling large tool corpora. By using reinforcement learning, a Qwen3-8B model is trained to perform tool selection, achieving performance that surpasses a 402‑billion‑parameter Llama‑4‑Maverick model on the ToolBench benchmark. The trained model also demonstrates transferability to different retrievers and generalization across new tool domains.

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arXiv Machine Learning

Oct 23

Training Small Language Models for Transferable Tool Selection

The paper presents a method for training a small, open-source language model to select appropriate tools from large tool corpora, addressing the bottleneck of tool selection for LLM agents. By using reinforcement learning, the authors demonstrate that a Qwen3-8B model can outperform a much larger Llama-4-Maverick model on the ToolBench benchmark. The trained model also transfers to different retrievers and generalizes to new tool domains.

By Zhaoyi Zhou, Akshayaa Magesh, Jeongyeol Kwon, Artem Kuriksha, Daniel Jiang, Andrea Zanette, Ayush Jain

llms agents reinforcement-learning

arXiv AI

Jul 8

PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models

arXiv:2607. 05441v1 Announce Type: cross Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks.

By Lorenzo Molfetta, Giacomo Frisoni, Nicol\`o Monaldini, Gianluca Moro

llms safety

arXiv AI

Aug 12

LLM Agents Factory: Retrieval of Domain-Specific LLM Agents

arXiv:2608. 09934v1 Announce Type: cross Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specialized behaviors.

By Vitalii Belov, Artyom Sosedka, Andrey Sakhovskiy, Elizaveta Kovtun, Artyom Boyarskikh, Semen Budennyy

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Hugging Face Trending Papers

Aug 11

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deploying these skills to accomplish real-world tasks.

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 29

Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction

arXiv:2607. 25718v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on invoking external tools to complete real-world tasks.

By Xinyi Hong, Pinjun Dong, Xinyang Yu, Binyan Jiang

llms agents benchmarks

arXiv AI

Aug 12

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

arXiv:2608. 10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution.

By Chenhao Dang, Siyuan Xiong, Conghui He, Weijia Li

llms agents reinforcement-learning benchmarks

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Training Small Language Models for Transferable Tool Selection

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