ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget

• URL: http://arxiv.org/abs/2604.01195v2
• Date: Thu, 02 Apr 2026 17:27:04 GMT
• Title: ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget
• Abstract summary: ORBIT is a training dataset with 20K reasoning-intensive queries with short verifiable answers.<n>Our framework, code and datasets are open-sourced and available publicly.
• Score: 56.6320292944541
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Search agents, which integrate language models (LMs) with web search, are becoming crucial for answering complex user queries. Constructing training datasets for deep research tasks, involving multi-step retrieval and reasoning, remains challenging due to expensive human annotation, or cumbersome prerequisites. In this work, we introduce ORBIT, a training dataset with 20K reasoning-intensive queries with short verifiable answers, generated using a frugal framework without relying on paid API services. The modular framework relies on four stages: seed creation, question-answer pair generation, and two stages of verification: self and external. ORBIT spans 15 domains and each training pair requires 4-5 reasoning steps, with external search verification required from the complete web. We train Qwen3-4B as the base model on ORBIT using GRPO and evaluate it on Wikipedia question answering tasks. Extensive experiment results demonstrate that ORBIT-4B achieves strong performance among sub-4B LLMs as search agents, proving the utility of synthetic datasets. Our framework, code and datasets are open-sourced and available publicly.

Related papers

• KARL: Knowledge Agents via Reinforcement Learning [63.627906947205624]
  We present a system for training enterprise search agents via reinforcement learning.<n> KARLBench is a multi-capability evaluation suite spanning six distinct search regimes.<n>We show that models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark.
  arXiv  Detail & Related papers  (2026-03-05T14:30:25Z)
• AgentIR: Reasoning-Aware Retrieval for Deep Research Agents [76.29382561831105]
  Deep Research agents generate explicit natural language reasoning before each search call.<n> Reasoning-Aware Retrieval embeds the agent's reasoning trace alongside its query.<n>DR- Synth generates Deep Research retriever training data from standard QA datasets.<n>AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch.
  arXiv  Detail & Related papers  (2026-03-04T18:47:26Z)
• DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search [61.77858432092777]
  We present DeepMMSearch-R1, the first multimodal large language model capable of performing on-demand, multi-turn web searches.<n>DeepMMSearch-R1 can initiate web searches based on relevant crops of the input image making the image search more effective.<n>We conduct extensive experiments across a range of knowledge-intensive benchmarks to demonstrate the superiority of our approach.
  arXiv  Detail & Related papers  (2025-10-14T17:59:58Z)
• Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning [23.104182075898297]
  Reasoning-augmented search agents, such as Search-R1, are trained to reason, search, and generate the final answer iteratively.<n>We train an LLM-based search agent with the native capability of query expansion through reinforcement learning.<n>With the assistance of the squeezer model, we discover that even a small-scale 3B LLM can demonstrate a strong capability of query expansion.
  arXiv  Detail & Related papers  (2025-10-11T04:23:30Z)
• Open Data Synthesis For Deep Research [17.22470203913576]
  We formalize Deep Research tasks with verifiable answers as Hierarchical Constraint Satisfaction Problems.<n>Existing benchmarks (e.g., Natural Questions, HotpotQA) fail to capture this complexity.<n>We introduce InfoSeek, a scalable scalable complex Deep Research tasks.
  arXiv  Detail & Related papers  (2025-08-30T06:02:56Z)
• From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data [5.336176993332404]
  Retrieval-Augmented Generation has emerged as the standard paradigm for answering questions on enterprise data.<n>We propose a new classification framework (L1-L5) to categorize systems based on data modalities and task complexity.<n>We evaluate four state-of-the-art platforms: LangChain, Azure AI Search, OpenAI, and Corvic AI.
  arXiv  Detail & Related papers  (2025-08-27T21:43:03Z)
• MMSearch-R1: Incentivizing LMMs to Search [49.889749277236376]
  We present MMSearch-R1, the first end-to-end reinforcement learning framework that enables on-demand, multi-turn search in real-world Internet environments.<n>Our framework integrates both image and text search tools, allowing the model to reason about when and how to invoke them guided by an outcome-based reward with a search penalty.
  arXiv  Detail & Related papers  (2025-06-25T17:59:42Z)
• On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering [25.57202500348071]
  This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models interact with a database. The task requires LLMs to strategically generate multiplesql queries to retrieve sufficient data from a database, to reason with the acquired context, and to synthesize them into a comprehensive analytical narrative. We propose and evaluate two interaction strategies, and provide a fine-grained analysis of the individual stages within the interaction.
  arXiv  Detail & Related papers  (2023-11-16T09:55:07Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)

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.