DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use

• URL: http://arxiv.org/abs/2603.11076v1
• Date: Tue, 10 Mar 2026 20:54:23 GMT
• Title: DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use
• Abstract summary: Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68.
• Score: 66.02634251098537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to remain executable and verifiable, while generalization demands coverage of diverse tool types, toolset combinations, and heterogeneous tool-use patterns. We propose DIVE, an evidence-driven recipe that inverts synthesis order, executing diverse, real-world tools first and reverse-deriving tasks strictly entailed by the resulting traces, thereby providing grounding by construction. DIVE scales structural diversity along two controllable axes, tool-pool coverage and per-task toolset variety, and an Evidence Collection--Task Derivation loop further induces rich multi-step tool-use patterns across 373 tools in five domains. Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Remarkably, controlled scaling analysis reveals that diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.

Related papers

• Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning [4.227963374833712]
  Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning.<n>We show that scaling task diversity is the dominant factor in achieving robust zero-shot transfer.
  arXiv  Detail & Related papers  (2026-09-03T11:04:23Z)
• SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents [64.59100414726556]
  Large language model (LLM) agents increasingly rely on harnesses that manage context, tools, and multi-turn execution.<n>Retrieval-augmented tool selection offers a natural alternative, but existing one-shot retrieval methods fail to align isolated tool descriptions with the agent's true task intention.<n>We propose SING, an intention-aware active tool discovery framework that builds an intention-tool graph linking user intentions, tool capabilities, and tool collaboration patterns.
  arXiv  Detail & Related papers  (2026-06-15T11:37:37Z)
• VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining [49.78591189918702]
  We introduce VisualToolChain-Bench (VTC-Bench) to evaluate tool-use proficiency in Multimodal Large Language Models (MLLMs)<n>To align with realistic computer vision pipelines, our framework features 32 diverse OpenCV-based visual operations.<n>For precise evaluation, we provide 680 curated problems structured across a nine-category cognitive hierarchy.
  arXiv  Detail & Related papers  (2026-03-16T09:31:44Z)
• Beyond Quantity: Trajectory Diversity Scaling for Code Agents [51.71414642763219]
  Trajectory Diversity Scaling is a data synthesis framework for code agents that scales performance through diversity rather than raw volume.<n> TDScaling integrates four innovations: (1) a Business Cluster mechanism that captures real-service logical dependencies; (2) a blueprint-driven multi-agent paradigm that enforces trajectory coherence; and (3) an adaptive evolution mechanism that steers toward long-tail scenarios.
  arXiv  Detail & Related papers  (2026-02-03T07:43:03Z)
• ToolTok: Tool Tokenization for Efficient and Generalizable GUI Agents [16.06309106596998]
  ToolTok is a novel paradigm of multi-step pathfinding for GUI agents.<n>We devise tools aligned with human interaction habits and represent each tool using learnable token embeddings.<n>We construct an easy-to-hard curriculum consisting of three tasks: token definition question-answering, pure text-guided tool selection, and simplified visual pathfinding.
  arXiv  Detail & Related papers  (2026-01-30T08:38:05Z)
• AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning [66.24374176797075]
  We introduce textbfAdaReasoner, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior.<n>AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that prioritizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage.
  arXiv  Detail & Related papers  (2026-01-26T16:04:43Z)
• Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning [68.00304954972232]
  Multimodal agents, which integrate a controller e.g., a vision language model, with external tools, have demonstrated remarkable capabilities in tackling complex multimodal tasks.<n>Existing approaches for training these agents depend on extensive human-annotated task-answer pairs and tool trajectories.<n>We propose an iterative tool usage exploration method for multimodal agents without any pre-collected data, namely SPORT.<n>SPORT has four iterative components: task synthesis, step sampling, step verification, and preference tuning.
  arXiv  Detail & Related papers  (2025-04-30T12:01:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.