Beyond Turn Limits: Training Deep Search Agents with Dynamic Context
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Authors: name: Qiaoyu Tang; name: Hao Xiang; name: Le Yu; name: Bowen Yu; name: Yaojie Lu; name: Xianpei Han; name: Le Sun; name: WenJuan Zhang; name: Pengbo Wang; name: Shixuan Liu; name: Zhenru Zhang; name: Jianhong Tu; name: Hongyu Lin; name: Junyang Lin
Published: 2025-10-09 14:31:39

Abstract: While recent advances in reasoning models have demonstrated cognitive
behaviors through reinforcement learning, existing approaches struggle to
invoke deep reasoning capabilities in multi-turn agents with long-horizon
interactions. We propose DeepMiner, a novel framework that elicits such
abilities by introducing high-difficulty training tasks and dynamic context
window. DeepMiner presents a reverse construction method to generate complex
but verifiable question-answer pairs from authentic web sources, which ensures
the challenge and reliability of training data while injecting cognitive
capabilities into multi-turn reasoning scenarios. We further design an elegant
yet effective dynamic context management strategy for both training and
inference, utilizing sliding window mechanisms while eliminating the dependency
on external summarization models, thereby efficiently empowering the model to
handle continuously expanding long-horizon contexts. Through reinforcement
learning on Qwen3-32B, we develop DeepMiner-32B, which achieves substantial
performance improvements across multiple search agent benchmarks. DeepMiner
attains 33.5% accuracy on BrowseComp-en, surpassing the previous best
open-source agent by almost 20 percentage points, and demonstrates consistent
improvements on BrowseComp-zh, XBench-DeepSearch, and GAIA. Notably, our
dynamic context management enables sustained interactions of nearly 100 turns
within standard 32k context length, effectively addressing the context
limitations that constrain existing multi-turn interaction systems.