REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents
Large language models are transitioning from generalpurpose knowledge engines to realworld problem solvers, yet optimizing them for deep search tasks remains challenging. The central bottleneck lies in the extreme sparsity of highquality search trajectories and reward signals, arising from the difficulty of scalable longhorizon task construction and the high cost of interactionheavy rollouts involving external tool calls. To address these challenges, we propose REDSearcher, a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization. Specifically, REDSearcher introduces the following improvements: (1) We frame task synthesis as a dualconstrained optimization, where task difficulty is precisely governed by graph topology and evidence dispersion, allowing scalable generation of complex, highquality tasks. (2) We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.(3) During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling substantially reducing the cost of collecting highquality trajectories for downstream training. (4) We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments. Across both textonly and multimodal searchagent benchmarks, our approach achieves stateoftheart performance. To facilitate future research on longhorizon search agents, we will release 10K highquality complex text search trajectories, 5K multimodal trajectories and 1K text RL query set, and together with code and model checkpoints.
6993dd2150fb2c0be4783cfa
Zheng Chu
6993dd2150fb2c0be4783cfb
Xiao Wang
6993dd2150fb2c0be4783cfc
Jack Hong
6993dd2150fb2c0be4783cfd
Huiming Fan
6993dd2150fb2c0be4783cfe
Yuqi Huang
6993dd2150fb2c0be4783cff
Yue Yang
6993dd2150fb2c0be4783d00
Guohai Xu
6993dd2150fb2c0be4783d01
Chenxiao Zhao
admin_assigned
2026-02-17T17:17:24.619Z
63fc5b724c57549ad5e54558
/avatars/1374c1e8969533dd7543959666f16d1a.svg
Chenxiao Zhao
ChenShawn
user
ChenShawn
6993dd2150fb2c0be4783d02
Cheng Xiang
6993dd2150fb2c0be4783d03
Shengchao Hu
6993dd2150fb2c0be4783d04
Dongdong Kuang
6993dd2150fb2c0be4783d05
Ming Liu
6993dd2150fb2c0be4783d06
Bing Qin
6993dd2150fb2c0be4783d07
Xing Yu
REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents
Large language models are transitioning from generalpurpose knowledge engines to realworld problem solvers, yet optimizing them for deep search tasks remains challenging. The central bottleneck lies in the extreme sparsity of highquality search trajectories and reward signals, arising from the difficulty of scalable longhorizon task construction and the high cost of interactionheavy rollouts involving external tool calls. To address these challenges, we propose REDSearcher, a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization. Specifically, REDSearcher introduces the following improvements: (1) We frame task synthesis as a dualconstrained optimization, where task difficulty is precisely governed by graph topology and evidence dispersion, allowing scalable generation of complex, highquality tasks. (2) We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.(3) During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling substantially reducing the cost of collecting highquality trajectories for downstream training. (4) We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments. Across both textonly and multimodal searchagent benchmarks, our approach achieves stateoftheart performance. To facilitate future research on longhorizon search agents, we will release 10K highquality complex text search trajectories, 5K multimodal trajectories and 1K text RL query set, and together with code and model checkpoints.
REDSearcher presents a unified framework for optimizing search agents through improved task synthesis, tool-augmented queries, midtraining capability enhancement, and simulated environments to address challenges in long-horizon search tasks.
task synthesis dual-constrained optimization graph topology evidence dispersion tool-augmented queries midtraining atomic capabilities knowledge planning function calling local simulated environment reinforcement learning search agents long-horizon tasks