We present ReSuMe, a general framework for mutual enhancement of dense retrieval systems and document summarizers through reinforcement learning. The framework jointly optimizes a language model for generating retrieval-oriented summaries and adapts the retrieval model to these summaries through alternating fine-tuning phases. We employ Group Relative Policy Optimization (GRPO) to fine-tune the language model based on retrieval relevance rather than linguistic quality alone, while the retrieval model is iteratively updated using contrastive learning on the generated summaries. This co-optimization process addresses the fundamental distribution shift problem that arises when retrieval models trained on full documents must operate on synthetic summaries during inference. By progressively reducing this distribution gap, our framework yields two key benefits: improved retrieval performance and a high-quality document summarizer optimized for retrieval tasks. We demonstrate our framework using Contriever on the MS-MARCO dataset, achieving consistent improvements of 13.2% in MRR@10 and 6.7% in Recall@100 over the baseline. The framework is model-agnostic and can be applied to enhance any dense retrieval system while simultaneously producing an effective document summarization model.

Machine Learning as a Service (MLaaS) has simplified access to powerful machine learning models but faces challenges in complying with the “right to be forgotten” while resisting adversarial threats. Machine Unlearning (MU) addresses these issues by enabling selective data removal from models. However, existing methods are slow, label-dependent, vulnerable to black-box attacks, and computationally impractical for large-scale MLaaS deployments. We introduce SALSA, a Secure, Adaptive, Label-Agnostic, Scalable Algorithm for efficient and robust machine unlearning tailored to classification tasks in MLaaS. SALSA redistributes the class-wise predicted probabilities of data to be forgotten and optimizes a novel loss function that minimizes the divergence between redistributed and predicted probabilities while anchoring model parameters near their initialization. This ensures simultaneous unlearning and generalization. SALSA requires neither labels nor access to the remaining data, making it ideal for MLaaS environments. It is exceptionally fast, achieving at least $25\times$ faster unlearning, on average, than the fastest baseline, while consistently outperforming five state-of-the-art MU techniques across eight metrics on benchmark datasets. Experiments on synthetic data show that SALSA’s altered decision boundaries closely approximate exact unlearning. Rigorous evaluations against state-of-the-art black-box attacks demonstrate its resilience to security threats. Thus, SALSA redefines practical machine unlearning, offering a scalable and resilient solution for safeguarding privacy in modern MLaaS systems.