arXiv:2606. 07074v1 Announce Type: cross Abstract: Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost.
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Large language models (LLMs) are increasingly extended into deep search agents that solve complex questions through multi-step interaction with external search and browsing tools. However, existing agents often incur substantial computational and interaction costs, generating lengthy trajectories that contain redundant queries, inefficient exploration, and irrelevant observations.
arXiv:2606. 27291v1 Announce Type: new Abstract: Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles.
Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present an end-to-end RLAIF (Reinforcement Learning from AI Feedback) framework to generate \emph{portable} job search queries, terms that abstract away seeker-specific identifiers while preserving generalizable qualifications.
arXiv:2601. 15141v2 Announce Type: replace Abstract: Agentic Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to utilize tools like Python interpreters for complex problem-solving.
arXiv:2607. 23263v1 Announce Type: new Abstract: Deciding whether a trajectory actually fulfills its instruction governs how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning.
The paper introduces AnySearch, a reinforcement‑learning framework that trains a single policy to perform budget‑aware search for large language models under any budget constraint. The training proceeds in two phases: first, the agent learns with explicit budget state injection and structured reasoning prompts under linearly decaying budgets; second, the scaffold is removed and the agent adapts to randomly sampled budgets that match deployment conditions. The reward combines answer accuracy and budget efficiency, with adaptive weighting to emphasize efficiency for high‑accuracy queries and reduce it for low‑accuracy ones. Experiments on seven QA benchmarks demonstrate that AnySearch outperforms baselines across all budget scales, generalizes to unseen constraints, and improves tool productivity without excessive token overhead.