InfoFlow: Reinforcing Search Agent Via Reward Density Optimization

• URL: http://arxiv.org/abs/2510.26575v1
• Date: Thu, 30 Oct 2025 15:03:21 GMT
• Title: InfoFlow: Reinforcing Search Agent Via Reward Density Optimization
• Abstract summary: Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search.<n>In this paper, we formalize this challenge as the textbfReward Density Optimization problem, which aims to improve the reward obtained per unit of exploration cost.<n>We introduce textbfInfoFlow, a systematic framework that tackles this problem from three aspects.
• Score: 37.266452141225415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search. However, its application is often hindered by low \textbf{Reward Density} in deep search scenarios, where agents expend significant exploratory costs for infrequent and often null final rewards. In this paper, we formalize this challenge as the \textbf{Reward Density Optimization} problem, which aims to improve the reward obtained per unit of exploration cost. This paper introduce \textbf{InfoFlow}, a systematic framework that tackles this problem from three aspects. 1) \textbf{Subproblem decomposition}: breaking down long-range tasks to assign process rewards, thereby providing denser learning signals. 2) \textbf{Failure-guided hints}: injecting corrective guidance into stalled trajectories to increase the probability of successful outcomes. 3) \textbf{Dual-agent refinement}: employing a dual-agent architecture to offload the cognitive burden of deep exploration. A refiner agent synthesizes the search history, which effectively compresses the researcher's perceived trajectory, thereby reducing exploration cost and increasing the overall reward density. We evaluate InfoFlow on multiple agentic search benchmarks, where it significantly outperforms strong baselines, enabling lightweight LLMs to achieve performance comparable to advanced proprietary LLMs.

Related papers

• LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards [53.339700196282905]
  Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task.<n>Existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals.<n>textscLongTraceRL produces training contexts that are far more challenging than those built by random sampling or one-shot search.
  arXiv  Detail & Related papers  (2026-05-29T17:51:40Z)
• OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents [50.79529228477821]
  We introduce OpenSearch-VL, a fully open-source recipe for training frontier multimodal deep search agents with agentic reinforcement learning.<n>First, we curated a dedicated pipeline to construct high-quality training data through Wikipedia path sampling, fuzzy entity rewriting, and source-anchor visual grounding.<n>Besides, we design a diverse tool environment that unifies text search, image search, OCR, cropping, sharpening, super-resolution, and perspective correction.
  arXiv  Detail & Related papers  (2026-05-06T17:50:38Z)
• REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents [40.38002661542917]
  REDSearcher is a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization.<n>We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.<n>During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling.<n>We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments.
  arXiv  Detail & Related papers  (2026-02-15T17:04:46Z)
• Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration [49.9937230730202]
  We propose Search-R2, a novel Actor-Refiner collaboration framework that enhances reasoning through targeted intervention.<n>Our approach decomposes the generation process into an Actor, which produces initial reasoning trajectories.<n>We show that Search-R2 consistently outperforms strong RAG and RL-based baselines across model scales.
  arXiv  Detail & Related papers  (2026-02-03T15:32:09Z)
• SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback [68.60326181052658]
  We propose an agentic pipeline that automatically generates high quality, difficulty-controlled deep search question-answer pairs.<n>Our pipeline, SAGE, consists of a data generator which proposes QA pairs and a search agent which attempts to solve the generated question.<n>Our intrinsic evaluation shows SAGE generates questions that require diverse reasoning strategies, while significantly increases the correctness and difficulty of the generated data.
  arXiv  Detail & Related papers  (2026-01-26T06:37:56Z)
• Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards [60.0970117192627]
  Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents.<n>Existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process.<n>We propose textbfCitation-aware RL Rewards (CaRR), a fine-grained reward framework for deep search agents.
  arXiv  Detail & Related papers  (2026-01-09T18:57:53Z)
• Search Self-play: Pushing the Frontier of Agent Capability without Supervision [14.889394507446477]
  Self-play training for deep search agents is proposed in this paper.<n>In this search self-play (SSP) game, the proposer and the solver co-evolve their agent capabilities through both competition and cooperation.<n>SSP can significantly improve search agents' performance uniformly on various benchmarks without any supervision.
  arXiv  Detail & Related papers  (2025-10-21T17:19:35Z)
• HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation [21.08814504507274]
  suboptimal search behaviors exist widely, such as over-search and under-search.<n>Current training methods, which typically rely on outcome-based rewards in a RL framework, lack the fine-grained control needed to address these inefficiencies.<n>We introduce HiPRAG, a training methodology that incorporates a fine-grained, knowledge-grounded process reward into the RL training.
  arXiv  Detail & Related papers  (2025-10-09T05:13:10Z)
• Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them [23.986035712600657]
  We propose a reasoning-driven pipeline to study effective reasoning behavior patterns in agentic search.<n>We identify four beneficial reasoning behaviors: Information Verification, Authority Evaluation, Adaptive Search, and Error Recovery.<n>We show that behavior priming yields over 35% gains in Llama3.2-3B and Qwen3-1.7B compared to directly training agentic search models with RL.
  arXiv  Detail & Related papers  (2025-10-08T00:20:35Z)
• Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents [19.31471304268234]
  We introduce DeSA (Decoupling Search-and-Answering), a simple two-stage training framework that explicitly separates search optimization from answer generation.<n>Across seven QA benchmarks, DeSA-trained agents consistently improve search behaviors, delivering substantially higher search recall and answer accuracy than outcome-only baselines.
  arXiv  Detail & Related papers  (2025-10-06T11:09:45Z)
• Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs [7.3517692707289415]
  We introduce Fathom-DeepResearch, an agentic system composed of two specialized models.<n>The first is Fathom-Search-4B, a DeepSearch model optimized for evidence-based investigation through live web search and targeted webpage querying.<n>The second is Fathom- Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports.
  arXiv  Detail & Related papers  (2025-09-28T22:58:11Z)
• DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL [60.47878242100153]
  We present DeepDive to advance deep search agents.<n>We propose a strategy to automatically synthesize complex, difficult, and hard-to-find questions from open knowledge graphs.<n>We apply end-to-end multi-turn reinforcement learning to enhance LLMs' long-horizon reasoning with deep search.
  arXiv  Detail & Related papers  (2025-09-12T17:52:35Z)
• RRO: LLM Agent Optimization Through Rising Reward Trajectories [52.579992804584464]
  Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks.<n>In practice, agents sensitive to the outcome of certain key steps which makes them likely to fail the task.<n>We propose Reward Rising Optimization (RRO) to mitigate this issue.
  arXiv  Detail & Related papers  (2025-05-27T05:27:54Z)
• FLARE: Faithful Logic-Aided Reasoning and Exploration [47.46564769245296]
  We introduce a novel approach for traversing the problem space using task decompositions.<n>We use the Large Language Models to plan a solution, soft-formalise the query into facts and predicates using a logic programming code.<n>Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers.
  arXiv  Detail & Related papers  (2024-10-14T19:39:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.