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This paper proposes RIMS, a three-stage preference optimization framework for small-scale language models in retrieval-augmented generation, using synthetic chain-of-thought data and a differentiable soft aggregation mechanism to improve robustness against noisy evidence. Experiments show consistent gains over baselines on multi-hop QA benchmarks.