Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analyses, posing a major challenge for current language models. Existing methods largely follow a plan-then-write paradigm, whose performance strongly depends on the quality of the initial outline. We propose a Writing As Reasoning Policy (WARP) framework, which enables models to dynamically revise outlines during report writing. In this policy, the agent alternates between Evidence-Based Drafting and Reasoning-Driven Deepening phases, jointly supporting information acquisition, knowledge refinement, and outline updating. We further introduce a Multi-Stage Agentic Training—including cold start, atomic skill RL, and holistic pipeline RL—that enables small models to operate the WARP effectively. Experiments on DeepResearch Bench, DeepConsult, and DeepResearch Gym show that our approach allows small models to surpass leading closed-source systems, particularly with substantial improvements in Insight metric.

In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL ability of LLMs can enhance their utilization and deepen our understanding of how this ability is acquired at the training stage. However, existing evaluation frameworks primarily focus on language abilities and knowledge, often overlooking the assessment of ICL ability. In this work, we introduce the ICLEval benchmark to evaluate the ICL abilities of LLMs, which encompasses two key sub-abilities: exact copying and rule learning. Through the ICLEval benchmark, we demonstrate that ICL ability is universally present in different LLMs, and model size is not the sole determinant of ICL efficacy. Surprisingly, we observe that ICL abilities, particularly copying, develop early in the pretraining process and stabilize afterward.

Utilizing Graphic User Interfaces (GUIs) for human-computer interaction is essential for accessing various digital tools. Recent advancements in Vision Language Models (VLMs) reveal significant potential for developing versatile agents that assist humans in navigating GUIs. However, current VLMs face challenges related to fundamental abilities, such as OCR and grounding, as well as a lack of knowledge about GUI elements functionalities and control methods. These limitations hinder their effectiveness as practical GUI agents. To address these challenges, we introduce GUICourse, a series of datasets for training visual-based GUI agents using general VLMs. First, we enhance the OCR and grounding capabilities of VLMs using the GUIEnv dataset. Next, we enrich the GUI knowledge of VLMs using the GUIAct and GUIChat datasets. Our experiments demonstrate that even a small-sized GUI agent (with 3.1 billion parameters) performs effectively on both single-step and multi-step GUI tasks. We further finetune our GUI agents on other GUI tasks with different action spaces (AITW and Mind2Web), and the results show that our agents are better than their baseline VLMs. Additionally, we analyze the impact of OCR and grounding capabilities through an ablation study, revealing a positive correlation with GUI navigation ability.