AutoTool: New Framework Enables LLM Agents to Dynamically Select and Adapt to Changing Tool Sets
Researchers introduced AutoTool, a training framework that allows large language models to dynamically select and integrate tools during reasoning tasks, rather than relying on fixed tool inventories. The framework uses a two-phase optimization approach combining supervised fine-tuning, reinforcement learning, and ranking techniques, trained on a dataset of 200,000 examples across 1,000+ tools. AutoTool demonstrated consistent performance improvements of 4.5% to 7.7% across mathematics, code generation, search-based QA, and multimodal reasoning benchmarks while generalizing to unseen tools.
AutoTool addresses a limitation in current agentic LLM systems that assume fixed tool inventories by introducing dynamic tool-selection capabilities. The framework employs a dual-phase optimization pipeline: first stabilizing reasoning trajectories through supervised fine-tuning and reinforcement learning, then refining multi-step tool selection using KL-regularized Plackett-Luce Ranking. The researchers created a 200,000-example dataset with explicit tool-selection rationales spanning 1,000+ tools and 100+ tasks across mathematics, science, code generation, and multimodal reasoning. Testing on two base models (Qwen3-8B and Qwen2.5-VL-7B), AutoTool achieved average performance gains of 6.4% in math and science reasoning, 4.5% in search-based question answering, 7.7% in code generation, and 6.9% in multimodal understanding. A key advantage is the framework's ability to generalize to unseen tools during inference, enabling adaptation to evolving toolsets.
What's missing
The paper does not discuss computational costs or training time comparisons with baseline methods, nor does it address potential failure modes when tools are unavailable or provide conflicting recommendations. The generalization claims to 'unseen tools' lack detail on how different tool types (e.g., APIs vs. local functions) are handled.
What different sources said
- arXiv cs.CLCenter
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
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