WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning
Summary
WideSeek-R1 is a multi-agent reinforcement learning framework designed for broad information seeking, exploring width scaling to achieve performance comparable to larger single agents with fewer parameters.
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Paper page - WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning
Source: https://huggingface.co/papers/2602.04634
Abstract
Multi-agent systems using reinforcement learning enable parallel information seeking with scalable orchestration, achieving performance comparable to larger single agents.
Recent advancements inLarge Language Models(LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling withmulti-agent systemsto address broadinformation seeking. Existingmulti-agent systemsoften rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, alead-agent-subagent frameworktrained viamulti-agent reinforcement learning(MARL) to synergize scalable orchestration andparallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. Extensive experiments show that WideSeek-R1-4B achieves an itemF1 scoreof 40.0% on theWideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.
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