AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning (2504.14858v3)

Abstract: Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded LLMs. However, standard RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions. In this work, we reinterpret RAG as Retrieval-Augmented Reasoning and identify a central but underexplored problem: \textit{Reasoning Misalignment}-the divergence between an LLM's internal reasoning trajectory and the evidential constraints provided by retrieval. To address this issue, we propose \textsc{AlignRAG}, a novel iterative framework grounded in Critique-Driven Alignment (CDA). At the heart of \textsc{AlignRAG} lies a \textit{contrastive critique synthesis} mechanism that generates retrieval-sensitive critiques while mitigating self-bias. This mechanism trains a dedicated retrieval-augmented \textit{Critic LLM (CLM)} using labeled critiques that distinguish between evidence-aligned and misaligned reasoning. Alignment signals for supervision are obtained through self-supervised or externally guided labeling strategies. The resulting CLM is explicitly optimized for evidence sensitivity, enabling it to detect and revise reasoning errors during inference without relying solely on self-generated feedback. Empirical evaluations show that our 8B-parameter CLM improves performance over the Self-Refine baseline by 12.1\% on out-of-domain tasks and outperforms a standard 72B-parameter CLM by 2.2\%, while remaining compatible with existing RAG architectures as a plug-and-play module. Overall, AlignRAG offers a principled solution for aligning model reasoning with retrieved evidence, substantially improving the factual reliability and robustness of RAG systems.

An Overview of AlignRAG: A Framework for Addressing Reasoning Misalignments in Retrieval-Augmented Generation

The paper "AlignRAG: An Adaptable Framework for Resolving Misalignments in Retrieval-Aware Reasoning of RAG" introduces a novel approach to enhance accuracy and robustness in Retrieval-Augmented Generation (RAG) systems. Traditional RAG applications, which combine LLMs with external data retrieval mechanisms, often experience reasoning misalignment—a discrepancy between the logical path taken by the model and the evidence retrieved. This paper identifies and addresses this core challenge by presenting AlignRAG, a framework that integrates Critique-Driven Alignment (CDA) at the test-time stage.

Core Contributions

1. Reconceptualization of RAG: The authors redefine RAG as a structured reasoning task rather than a simple generation task. The framework identifies reasoning misalignment in three phases—relevance assessment, query-evidence mapping, and evidence-integrated justification—highlighting areas where LLM reasoning deviates from evidential constraints.
2. Introduction of Critique-Driven Alignment: AlignRAG proposes the CDA mechanism, which iteratively refines reasoning trajectories. The Critic LLM (CLM) is employed to detect misalignments and produce critiques for output adjustment, allowing the model to self-correct dynamically based on evidence alignment.
3. Contrastive Critiques and Critic LLM Training: The framework includes innovative methods to produce contrastive critiques from preference-aware reasoning trajectories and deploys a new learning paradigm for training the CLM, improving its ability to pinpoint and resolve reasoning errors effectively.

Experimental Results

AlignRAG demonstrates statistically significant improvements across multiple benchmarks, achieving state-of-the-art results in seven diverse tasks and outperforming existing methods, including those focusing on retrieval quality and coherence enhancements like InstructRAG. Notably, AlignRAG enhances generalization capabilities, showing robust performance in both informative and noisy retrieval settings, and substantial gains even when integrated into existing detailed RAG pipelines as a plug-and-play module.

Implications and Future Directions

The effectiveness of AlignRAG indicates a promising direction for further advancements in AI-driven reasoning systems. By mitigating reasoning misalignment, this framework not only improves model accuracy but also enhances the model's capacity to function across varied domains and unpredictable environments. Future developments could explore the refinement of critique learning processes, optimize mechanisms for even faster real-time alignment, and expand applications to more diverse knowledge-intensive tasks.

In summary, this paper offers a significant leap in comprehension and capability for RAG models, moving from static error corrections to dynamic, context-aware optimization processes that embolden LLMs with a refined sense of logic and evidence retention. This paper sets the groundwork for ongoing research in the domain of retrieval-aware reasoning and highlights the potential impact of realigning generation models with structured, evidence-driven frameworks.