arXiv:2608. 05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer.
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...
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. 07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence.
arXiv:2602. 08335v2 Announce Type: replace Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving complex problems.
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
arXiv:2606. 01830v1 Announce Type: new Abstract: Recent LLM search agents use reinforcement learning with verifiable rewards (RLVR) to learn search-augmented reasoning from outcome rewards.
arXiv:2606. 10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering.
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.
The paper introduces BRIDGE, a bilevel optimization framework that jointly trains a large language model (LLM) and a retriever for agentic reinforcement learning (ARL). It demonstrates that adapting the retriever before the policy yields better rewards, and that BRIDGE outperforms existing methods on seven open‑domain QA benchmarks and medical QA tasks, achieving significant gains in accuracy and reasoning quality.
arXiv:2605. 01248v3 Announce Type: replace Abstract: Reinforcement learning (RL) post-training has enabled newer capabilities in models, such as agentic tool-use for search.
The paper introduces DAC (Divide and Cooperate), a role‑decomposed training framework that separates evidence acquisition (searcher) and response generation (generator) in agentic search systems. DAC uses task‑specific external verification signals to provide cross‑verification rewards, allowing the generator to abstain when evidence is insufficient and to assign credit appropriately to each role. Experiments on seven QA benchmarks and two model backbones show that DAC consistently outperforms single‑agent and multi‑agent baselines, with gains attributed to coordinated improvements in both search and generation.