Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2505.20825v1
• Date: Tue, 27 May 2025 07:34:41 GMT
• Title: Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
• Abstract summary: Long-form question answering (LFQA) presents unique challenges for large language models.<n>RioRAG is a novel reinforcement learning framework that advances long-form RAG through reinforced informativeness optimization.
• Score: 77.10390725623125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-form question answering (LFQA) presents unique challenges for large language models, requiring the synthesis of coherent, paragraph-length answers. While retrieval-augmented generation (RAG) systems have emerged as a promising solution, existing research struggles with key limitations: the scarcity of high-quality training data for long-form generation, the compounding risk of hallucination in extended outputs, and the absence of reliable evaluation metrics for factual completeness. In this paper, we propose RioRAG, a novel reinforcement learning (RL) framework that advances long-form RAG through reinforced informativeness optimization. Our approach introduces two fundamental innovations to address the core challenges. First, we develop an RL training paradigm of reinforced informativeness optimization that directly optimizes informativeness and effectively addresses the slow-thinking deficit in conventional RAG systems, bypassing the need for expensive supervised data. Second, we propose a nugget-centric hierarchical reward modeling approach that enables precise assessment of long-form answers through a three-stage process: extracting the nugget from every source webpage, constructing a nugget claim checklist, and computing rewards based on factual alignment. Extensive experiments on two LFQA benchmarks LongFact and RAGChecker demonstrate the effectiveness of the proposed method. Our codes are available at https://github.com/RUCAIBox/RioRAG.

Related papers

• NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains [14.980207267884674]
  Retrieval-Augmented Generation (RAG) improvements do not consistently translate to commensurate gains in downstream reasoning.<n>We propose the Recall Conversion Rate (RCR), a novel evaluation metric to quantify the contribution of retrieval to reasoning accuracy.<n>We introduce the NeocorRAG framework, which achieves holistic retrieval quality optimization by systematically mining and utilizing Evidence Chains.
  arXiv  Detail & Related papers  (2026-04-30T13:37:01Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning [69.87510139069218]
  Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs)<n>Recent progress has advanced text-based RAG to multi-turn reasoning through Reinforcement Learning (RL)<n>We introduce model, an RL-based framework that enables LLMs to perform multi-turn and adaptive graph-text hybrid RAG.
  arXiv  Detail & Related papers  (2025-12-10T10:05:31Z)
• Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding [59.60915947702282]
  Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs)<n>Existing RLVR methods often suffer from exploration inefficiency due to mismatches between the training data's difficulty and the model's capability.<n>We propose SEELE, a novel supervision-aided RLVR framework that dynamically adjusts problem difficulty to stay within the high-efficiency region.
  arXiv  Detail & Related papers  (2025-09-08T17:36:21Z)
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs [50.820065021136024]
  DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs)<n>Recent methods have attempted to replicate R1's reasoning capabilities in multimodal settings.<n>We propose TACO, a novel reinforcement learning algorithm for visual reasoning.
  arXiv  Detail & Related papers  (2025-05-27T06:30:48Z)
• Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning [80.27561080938747]
  We propose a systematic framework, CANOE, to improve the faithfulness of large language models (LLMs) in both short-form and long-form generation tasks without human annotations.<n>Also, we propose Dual-GRPO, a rule-based reinforcement learning method that includes three tailored rule-based rewards derived from synthesized short-form QA data.<n> Experimental results show that CANOE greatly improves the faithfulness of LLMs across 11 different downstream tasks, even outperforming the most advanced LLMs.
  arXiv  Detail & Related papers  (2025-05-22T10:10:07Z)
• Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning [45.10424242207931]
  Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs)<n>We introduce a novel method ReasonRAG that automatically constructs RAG-ProGuide, a high-quality dataset providing process-level rewards for query generation, evidence extraction, and answer generation.<n>With the process-level policy optimization, the proposed framework empowers LLMs to autonomously invoke search, generate queries, extract relevant evidence, and produce final answers.
  arXiv  Detail & Related papers  (2025-05-20T08:21:00Z)
• Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps [16.84310001807895]
  This paper introduces a model-agnostic approach that can be applied to A-RAG methods.<n>Specifically, we use cache access and parallel generation to speed up the prefilling and decoding stages respectively.
  arXiv  Detail & Related papers  (2025-05-19T05:39:38Z)
• Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing [4.874077691069634]
  Retrieval Augmented Generation (RAG) has shown strong capability in enhancing language models' knowledge and reducing AI generative hallucinations.<n>Current multi-round RAG systems may continue searching even when enough information has already been retrieved.<n>This paper introduces a new framework, SIM-RAG, to explicitly enhance RAG systems' self-awareness and multi-round retrieval capabilities.
  arXiv  Detail & Related papers  (2025-05-05T17:39:35Z)
• DeepRAG: Thinking to Retrieval Step by Step for Large Language Models [92.87532210660456]
  We propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP)<n>By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step.<n> Experiments show that DeepRAG improves retrieval efficiency while improving answer accuracy by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG [36.754491649652664]
  Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. This paper investigates the detrimental impact of retrieved "hard negatives" as a key contributor. To mitigate this and enhance the robustness of long-context LLM-based RAG, we propose both training-free and training-based approaches.
  arXiv  Detail & Related papers  (2024-10-08T12:30:07Z)
• FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering [11.73887020240588]
  We propose a novel outline-enhanced generator to achieve clear logic in the generation of multifaceted answers. Then we propose a factuality optimization method based on a carefully designed doubly fine-grained RLHF framework. In particular, when applying our method to Llama2-7B-chat, the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly used metrics.
  arXiv  Detail & Related papers  (2024-06-19T19:06:36Z)

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.