AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language models. Most existing approaches follow a plan-then-write paradigm, whose performance heavily depends on the quality of the initial outline. However, constructing a comprehensive outline itself demands strong reasoning ability, causing current deep research systems to rely almost exclusively on closed-source or online large models. This reliance raises practical barriers to deployment and introduces safety and privacy concerns for user-authored data. In this work, we present AgentCPM-Report, a lightweight yet high-performing local solution composed of a framework that mirrors the human writing process and an 8B-parameter deep research agent. Our framework uses a Writing As Reasoning Policy (WARP), which enables models to dynamically revise outlines during report generation. Under this policy, the agent alternates between Evidence-Based Drafting and Reasoning-Driven Deepening, jointly supporting information acquisition, knowledge refinement, and iterative outline evolution. To effectively equip small models with this capability, we introduce a Multi-Stage Agentic Training strategy, consisting of cold-start, atomic skill RL, and holistic pipeline RL. Experiments on DeepResearch Bench, DeepConsult, and DeepResearch Gym demonstrate that AgentCPM-Report outperforms leading closed-source systems, with substantial gains in Insight.
6989512bbeecc443208d2656
Yishan Li
6989512bbeecc443208d2657
Wentong Chen
claimed_verified
2026-02-09T08:29:47.521Z
64f5abc2e8f27f20a067a596
/avatars/d0eac39488fac0c9c08d76109cabaa9f.svg
cwt
yiye2023
user
yiye2023
6989512bbeecc443208d2658
Yukun Yan
6989512bbeecc443208d2659
Mingwei Li
claimed_verified
2026-02-10T09:07:42.488Z
650be89d2158362826fa6a2d
/avatars/ede42a71fbbb72289c437926909703fd.svg
Li Mingwei
Ming0614
user
Ming0614
6989512bbeecc443208d265a
Sen Mei
claimed_verified
2026-02-11T11:20:23.396Z
647f2b5435bc6d6aa5fa8b7b
/avatars/9cf905c09f842769e4ea29cd5bc56efd.svg
Sen Mei
msss123
user
msss123
6989512bbeecc443208d265b
Xiaorong Wang
6989512bbeecc443208d265c
Kunpeng Liu
6989512bbeecc443208d265d
Xin Cong
6989512bbeecc443208d265e
Shuo Wang
6989512bbeecc443208d265f
Zhong Zhang
6989512bbeecc443208d2660
Yaxi Lu
6989512bbeecc443208d2661
Zhenghao Liu
6989512bbeecc443208d2662
Yankai Lin
6989512bbeecc443208d2663
Zhiyuan Liu
6989512bbeecc443208d2664
Maosong Sun
AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language models. Most existing approaches follow a plan-then-write paradigm, whose performance heavily depends on the quality of the initial outline. However, constructing a comprehensive outline itself demands strong reasoning ability, causing current deep research systems to rely almost exclusively on closed-source or online large models. This reliance raises practical barriers to deployment and introduces safety and privacy concerns for user-authored data. In this work, we present AgentCPM-Report, a lightweight yet high-performing local solution composed of a framework that mirrors the human writing process and an 8B-parameter deep research agent. Our framework uses a Writing As Reasoning Policy (WARP), which enables models to dynamically revise outlines during report generation. Under this policy, the agent alternates between Evidence-Based Drafting and Reasoning-Driven Deepening, jointly supporting information acquisition, knowledge refinement, and iterative outline evolution. To effectively equip small models with this capability, we introduce a Multi-Stage Agentic Training strategy, consisting of cold-start, atomic skill RL, and holistic pipeline RL. Experiments on DeepResearch Bench, DeepConsult, and DeepResearch Gym demonstrate that AgentCPM-Report outperforms leading closed-source systems, with substantial gains in Insight.
AgentCPM-Report presents a lightweight local solution for deep research report generation using a Writing As Reasoning Policy framework and multi-stage agentic training to enhance small models' reasoning and outline evolution capabilities.
Writing As Reasoning Policy WARP Evidence-Based Drafting Reasoning-Driven Deepening Multi-Stage Agentic Training cold-start atomic skill RL holistic pipeline RL deep research agent insight-driven analysis plan-then-write paradigm