arXiv:2605. 09038v3 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries.
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arXiv:2608. 12720v1 Announce Type: cross Abstract: While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this memory are rarely treated as evolvable components.
The paper introduces a search‑aware reinforcement learning framework for multi‑component query understanding in Roblox game search. It first uses teacher‑student supervised fine‑tuning to create a schema‑compliant policy, then applies reinforcement learning that optimizes each query‑understanding component with component‑specific rewards derived from live search engine interactions. Experiments show that this approach improves per‑component utility and overall search quality, raising NDCG@20 by 8.9 points over the supervised baseline and 3.5 points over a single end‑to‑end reward strategy.
SkillFM is a generative framework that creates task‑conditioned textual skills for large language model agents without relying on manual skill banks or reinforcement learning. It encodes skills into a continuous latent space using a codec and trains a conditional flow model with improved MeanFlow, allowing single‑step latent sampling at inference. The sampled latent is decoded by an LLM into textual guidance, and the method outperforms other vector‑based skill approaches on ALFWorld, Search‑QA, and other tasks.
arXiv:2607. 29468v1 Announce Type: new Abstract: Self-play agents can generate training problems without questions from target benchmarks, but their curricula lack persistent state: failures affect gradients yet do not explicitly shape future practice.
SEEK (Skill‑Routed Evaluation with Evolvable Knowledge) is a framework that externalizes search evaluation criteria into a skill bank, dynamically routes relevant skills for each query‑result pair, and uses a task‑adapted listwise evaluator to generate page‑level judgments and failure‑mode attribution. It employs a two‑stage training pipeline to align evaluation with human preferences and a replay‑gated skill bank to incorporate new evaluation knowledge without retraining the model. Experiments on industrial short‑video search demonstrate that SEEK improves listwise quality evaluation accuracy and significantly advances attribution diagnosis, leading to its deployment at Kuaishou with over 400 million daily active users.
Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially improved skill retrieval due to the increasing skill libraries, retrieving a plausible skill bundle does not guarantee that executing it is worthwhile.