RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation
Small language models in retrieval-augmented generation are highly sensitive to noisy evidence. This paper introduces RIMS, a three-stage preference optimization framework featuring synthetic chain-of-thought data generation, a differentiable soft aggregation mechanism, and preference optimization. Experiments show consistent gains on multi-hop QA benchmarks.
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[Submitted on 17 Jul 2026]
Title:RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generation
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Abstract:Small-scale language models (SLMs) are attractive for retrieval-augmented generation (RAG) in resource-constrained settings, but their limited capacity makes them highly sensitive to noisy or spurious retrieved evidence. Existing preference-based methods such as RoseRAG select only the hardest single preference pair via hard argmin/argmax, discarding the remaining signal; others treat multiple pairs as independent binary comparisons, resulting in low data utilization. We propose RIMS, a three-stage preference optimization framework comprising (1) synthetic chain-of-thought preference data generation via rejection sampling using the target SLM itself without relying on proprietary models, (2) a differentiable soft aggregation mechanism that replaces hard selection with a smooth operator, preserving gradient signal from all preference pairs while retaining the discriminative structure of margin-aware selection, and (3) preference optimization with the smoothed objective applied to multiple alignment algorithms. We theoretically show that the smoothed approximation admits a controllable error bound and that smooth aggregation yields provably tighter gradient alignment to the oracle objective than hard selection. Experiments on four multi-hop question answering benchmarks show that our approach outperforms state-of-the-art baselines across multiple SLM backbones, achieving consistent gains in Exact Match and F1 under noisy retrieval conditions. Our implementation is available at this https URL.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.16431 [cs.CL]
(or arXiv:2607.16431v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.16431
arXiv-issued DOI via DataCite (pending registration)
Journal reference: COLM 2026
Submission history
From: Haoyu Wang [view email] [v1] Fri, 17 Jul 2026 18:24:19 UTC (1,224 KB)
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