Recent advances in Reinforcement Learning (RL) have substantially improved the capabilities of autonomous search agents, enabling sophisticated planning, and iterative retrieval over dynamic information sources. However, optimizing language models for specialized search behaviors often incurs an alignment tax, where gains in search performance come at the expense of general-purpose capabilities, limiting their effectiveness as universal assistants.
arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.
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.
arXiv:2608. 12764v1 Announce Type: cross Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment.
The paper introduces Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning of large language models cumulative. In the first stage, a multi-agent pipeline uses Monte Carlo Tree Search to explore training strategies while a Distillation Agent records task-specific insights and cross-task confidence scores into a structured repository. In the second stage, SAGE retrieves relevant experience from this repository to guide training on new tasks, achieving a 12.4‑percentage‑point improvement over a baseline pipeline without accumulated experience on nine unseen tasks.
arXiv:2607. 24850v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons.
IterSynth introduces a role-decoupled, iterative synthesis framework for deep search agents, separating planning and synthesis into distinct Planner and Synthesizer modules that maintain a persistent summary state. This design mitigates role coupling and context noise, while the new Role-Decoupled Policy Optimization (RDPO) enhances training by combining outcome rewards with turn-level rubric evaluations. Experiments on five long-horizon benchmarks show IterSynth-8B outperforming prior ≤8B agents by 4.2% and delivering significant zero-shot gains over ReAct on proprietary models.
OPDSearch+ introduces a two‑stage distillation framework for search‑augmented reasoning that eliminates the need for task‑specific teacher fine‑tuning. In the first stage, a frozen off‑the‑shelf instruct model guides a student through live search interactions using a per‑position forward KL objective, transferring reasoning decomposition and evidence integration skills. The second stage refines this student with reinforcement learning, achieving performance surpassing RL alone and outperforming all prior 3B‑parameter baselines on seven QA benchmarks, including 13.1% improvement on HotpotQA and 8.5% on 2WikiMultihopQA.
arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.
Search-augmented reasoning remains difficult for small language models. On-policy distillation (OPD) from trained teachers offers a promising direction, but suffers from two issues: (1) high-quality m...
arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.
arXiv:2605. 11611v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for training agentic retrieval-augmented generation (RAG) systems from outcome-only supervision.
Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independent episodes, while existing approaches to skill learning either focus on repeated attempts of one task or use pipelines with multiple stages that entangle extraction, retrieval, and execution.