Beyond Turn Limits: Training Deep Search Agents with Dynamic Context
  Window
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.
68e8795995e8e6771df38a22
Qiaoyu Tang
claimed_verified
2025-10-10T04:32:02.610Z
64ed96f3067fbb625f83766a
/avatars/e8794ba19bdd965615bd9ead7df77d7d.svg
Qiaoyu Tang
user
TangQiaoYu
68e8795995e8e6771df38a23
Hao Xiang
68e8795995e8e6771df38a24
Le Yu
68e8795995e8e6771df38a25
Bowen Yu
68e8795995e8e6771df38a26
Yaojie Lu
68e8795995e8e6771df38a27
Xianpei Han
68e8795995e8e6771df38a28
Le Sun
68e8795995e8e6771df38a29
WenJuan Zhang
68e8795995e8e6771df38a2a
Pengbo Wang
68e8795995e8e6771df38a2b
Shixuan Liu
68e8795995e8e6771df38a2c
Zhenru Zhang
claimed_verified
2025-10-17T04:14:30.417Z
64704e973601bb7b06643e98
/avatars/52e51f4d1be6769e4397b8be2799cf32.svg
Zhenru Zhang
user
Zhenru
68e8795995e8e6771df38a2d
Jianhong Tu
68e8795995e8e6771df38a2e
Hongyu Lin
68e8795995e8e6771df38a2f
Junyang Lin
Beyond Turn Limits: Training Deep Search Agents with Dynamic Context
  Window
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.
DeepMiner, a framework using high-difficulty training tasks and dynamic context management, enhances multi-turn reasoning agents through reinforcement learning, achieving significant performance improvements across benchmarks.
reinforcement learning multi-turn agents long-horizon interactions high-difficulty training tasks dynamic context window reverse construction method question-answer pairs sliding window mechanisms dynamic context management BrowseComp-en BrowseComp-zh XBench-DeepSearch GAIA