7 accepted papers
LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relative Policy Optimization (GRPO) discard this rich entity information, relying instead on sparse, outcome-based rewards. T…
Although autoregressive language models demonstrated remarkable performance across various tasks, their effectiveness in symbolic reasoning and decision-making scenarios remains constrained. Recent research indicates that training language models to emulate symbolic search algorithms (e.g. depth-fi
To significantly advance the capabilities of open-source web agents, we present WebSailor-V2, a complete post-training pipeline encompassing data construction, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Our methodology features two key innovations: (1) On the data front, we devel…
Retrieval-augmented generation (RAG) enables large language models (LLMs) to address queries beyond their internal knowledge by integrating domain knowledge in specialized corpus, which necessitates the generation of benchmarks on specific corpus to evaluate RAG systems. However, existing automated
The rapid advancement of large language models (LLMs) has transformed the landscape of agentic information seeking capabilities through the integration of tools such as search engines and web browsers. However, current mainstream approaches for enabling LLM web search proficiency face significant ch
Addressing intricate real-world problems necessitates in-depth information seeking and multi-step reasoning. Recent progress in agentic systems, exemplified by Deep Research, underscores the potential for autonomous multi-step research. In this work, we present a cohesive paradigm for building end…
CLIP has demonstrated remarkable generalization across diverse downstream tasks. By aligning images and texts in a shared feature space, they enable zero-shot classification via hand-crafted prompts. However, recent studies have shown that hand-crafted prompts may be unsuitable in practical applicat…