Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents

• URL: http://arxiv.org/abs/2604.04651v1
• Date: Mon, 06 Apr 2026 13:00:38 GMT
• Title: Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents
• Abstract summary: Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents.<n>We find that despite possessing less parametric knowledge, Small Language Models (SLMs) invoke search tools less frequently and are more prone to hallucinations.<n>We propose policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence.
• Score: 26.112172869788846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents. Consequently, recent work has focused on distilling agentic behaviors from LLMs into Small Language Models (SLMs). Through comprehensive evaluation on complex multi-hop reasoning tasks, we find that despite possessing less parametric knowledge, SLMs invoke search tools less frequently and are more prone to hallucinations. To address this issue, we propose \policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence. Compared to agent distillation from LLMs, our approach improves performance by 17.3 scores on Bamboogle and 15.3 scores on HotpotQA, achieving LLM-level results across benchmarks. Our further analysis reveals that adaptive search strategies in SLMs often degrade performance, highlighting the necessity of consistent search behavior for reliable reasoning.

Related papers

• Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search [56.78490647843876]
  Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use.<n>We propose bfM-ASK, a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context.
  arXiv  Detail & Related papers  (2026-01-08T08:13:27Z)
• 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)
• RE-Searcher: Robust Agentic Search with Goal-oriented Planning and Self-reflection [55.125987985864896]
  We present a systematic analysis that quantifies how environmental complexity induces fragile search behaviors.<n>We propose a simple yet effective approach to instantiate a search agent, RE-Searcher.<n>This combination of goal-oriented planning and self-reflection enables RE-Searcher to resist spurious cues in complex search environments.
  arXiv  Detail & Related papers  (2025-09-30T10:25:27Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning [6.327006563699527]
  We introduce SearchExpert, a training method for large language models (LLMs) driven search agents.<n>We reformulate the search plan in an efficient natural language representation to reduce token consumption.<n>To improve reasoning-intensive search capabilities, we propose the reinforcement learning from search feedback.
  arXiv  Detail & Related papers  (2025-05-24T19:00:36Z)
• SEM: Reinforcement Learning for Search-Efficient Large Language Models [26.075903427834838]
  Large Language Models (LLMs) have demonstrated their capabilities not only in reasoning but also in invoking external tools.<n>Existing reinforcement learning approaches often lead to redundant search behaviors, resulting in inefficiencies and over-cost.<n>We propose SEM, a novel post-training reinforcement learning framework that explicitly trains LLMs to optimize search usage.
  arXiv  Detail & Related papers  (2025-05-12T09:45:40Z)
• ZeroSearch: Incentivize the Search Capability of LLMs without Searching [69.55482019211597]
  We introduce ZeroSearch, a framework that incentivizes the capabilities of large language models to use a real search engine with simulated searches during training.<n>Our approach begins with lightweight supervised fine-tuning to transform the LLM into a retrieval module capable of generating both useful and noisy documents.
  arXiv  Detail & Related papers  (2025-05-07T17:30:22Z)
• OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM [15.260794368585692]
  We propose OR-LLM-Agent, an AI agent framework built on reasoning LLMs for automated Operations Research problem solving.<n>We show that OR-LLM-Agent utilizing DeepSeek-R1 in its framework outperforms advanced methods, including GPT-o3, Gemini 2.5 Pro, DeepSeek-R1, and ORLM, by at least 7% in accuracy.
  arXiv  Detail & Related papers  (2025-03-13T03:40:50Z)
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning [87.30285670315334]
  textbfR1-Searcher is a novel two-stage outcome-based RL approach designed to enhance the search capabilities of Large Language Models.<n>Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start.<n>Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.
  arXiv  Detail & Related papers  (2025-03-07T17:14:44Z)
• Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning [56.82041895921434]
  Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities. When used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2024-03-29T03:48:12Z)
• More Agents Is All You Need [16.372072265248192]
  We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated.
  arXiv  Detail & Related papers  (2024-02-03T05:55:24Z)

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.