Learnable Retrieval Enhancer (LRE)

• Learnable Retrieval Enhancer (LRE) is a module that refines retrieval by adaptively weighting tokens, adjusting query embeddings, or compressing evidence for better alignment with downstream tasks.
• It integrates into retrieval-enhanced systems using methods like contrastive learning, reinforcement learning, and hyperbolic metric learning to optimize relevance scoring.
• Experimental results demonstrate improved nDCG scores, enhanced robustness against distractors, and increased efficiency across both text and multimodal retrieval applications.

Searching arXiv for the cited LRE-related papers to ground the article in current literature. arXiv search: "Learnable Retrieval Enhancer" Learnable Retrieval Enhancer (LRE) denotes a learnable module that improves how external information is selected, represented, or retained for a downstream model. Within retrieval-enhanced machine learning, this role can be situated inside a broader system of the form $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N})$, where a parametric predictor consumes outputs from one or more information-access components (Zamani et al., 2022). In the literature, LRE appears both as a generic descriptor and as a named module. In learned sparse retrieval, it is defined as a neural module that assigns an importance weight to each token in a large vocabulary and implicitly expands the original text with semantically related terms while preserving compatibility with inverted indices (Doshi et al., 2024). Other instantiations operate on query embeddings to suppress logical distractors (Zhang et al., 1 Feb 2026), on hyperbolic retrieval of reference reports for radiology report generation (Zhou et al., 10 Jul 2025), or on evidence extraction in retrieval-augmented generation (RAG) through a rationale-then-extract policy (Zhao et al., 21 Jul 2025). This suggests that LRE is best understood as a functional category of learnable retrieval augmentation rather than a single fixed architecture.

1. Placement within retrieval-enhanced machine learning

Retrieval-Enhanced Machine Learning (REML) recasts a predictive model as a two-part system: a conventional parametric learner that reasons and one or more non-parametric retrievers that supply it with external information on demand. In its most general form,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$

and training can be written as

$$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$

A retrieval submodule must support querying, retrieval, and response utilization; a typical parameterization embeds queries and documents in a shared vector space and scores them by inner product, with indexing used for sublinear search (Zamani et al., 2022).

Within this framework, an LRE is not the entire REML system but a learnable component that alters retrieval behavior or the form of retrieved evidence. Depending on the paper, it may act by reweighting vocabulary terms, adjusting query embeddings, learning a ranking metric over retrieved references, or compressing retrieved passages into concise evidence. The shared objective is to improve the utility of external memory without requiring the base model to absorb all relevant knowledge parametrically.

Work	LRE role	Retrieval substrate
"Mistral-SPLADE: LLMs for better Learned Sparse Retrieval" (Doshi et al., 2024)	token weighting and semantic expansion	inverted index
"Logic-Oriented Retriever Enhancement via Contrastive Learning" (Zhang et al., 1 Feb 2026)	query-side logical boundary formation	ANN over frozen document embeddings
"Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation" (Zhou et al., 10 Jul 2025)	hyperbolic nearest-neighbor reference report retrieval	offline report database
"Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation" (Zhao et al., 21 Jul 2025)	rationale and evidence extraction from top-$k$ passages	DPR over Wikipedia

A plausible implication is that the term emphasizes learnability at the retrieval interface rather than any specific modality, loss, or index structure.

2. Sparse lexical expansion as an LRE

In learned sparse retrieval, an LRE is explicitly defined as a neural module that, given a query or document, assigns an importance weight to each token in a large vocabulary and implicitly expands the original text with semantically related terms, yielding a high-dimensional sparse representation (Doshi et al., 2024). This formulation is designed to bridge classical bag-of-words retrieval and vector-based retrieval while preserving compatibility with inverted indices.

"Mistral-SPLADE: LLMs for better Learned Sparse Retrieval" instantiates this idea with a decoder-only LLM. The backbone is Mistral-7B. To compensate for unidirectional attention, each input sequence is concatenated twice and only the second occurrence’s token representations are pooled. Low-rank adapters are injected into all attention and feed-forward layers with rank $=16$, $\alpha=8$, and dropout $=0.1$, while the original embedding and output layers remain frozen to preserve interpretability (Doshi et al., 2024).

Token-level scoring is obtained by passing the final LM-head logit $w_{ij}$ through ReLU and log-saturation, then max-pooling across tokens:

$$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$

The resulting sparse vector $W\in\mathbb R^{|V|}$ is used for retrieval by sparse dot product,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$0

Training combines an InfoNCE ranking loss with FLOPS regularization,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$1

with $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$2 ramped up quadratically from step $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$3 to step $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$4k and then held constant until $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$5k steps (Doshi et al., 2024).

The reported experimental setting uses a $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$6M-example subset of Sentence-Transformer datasets, only in-batch negatives, batch size $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$7, input length $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$8 tokens echoed to $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$9 internally, Adam with learning rate $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$0, and $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$1 NVIDIA A100 80 GB GPUs. On BEIR zero-shot retrieval over $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$2 tasks, Echo-Mistral-SPLADE attains an average nDCG@10 of $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$3, compared with $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$4 for SPLADEv3, $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$5 for Elser V2, and $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$6 for BM25 (Doshi et al., 2024). Qualitative inspection of expansions for the query “first person to land on the moon” yields terms such as “Apollo 11,” “Armstrong,” and “NASA,” illustrating the term-expansion interpretation of the LRE.

3. Query-side logical enhancement in dense retrieval

"Logic-Oriented Retriever Enhancement via Contrastive Learning" presents LORE as a learnable retrieval enhancer for knowledge-intensive queries that involve complex logical relations (Zhang et al., 1 Feb 2026). LORE sits on top of any bi-encoder retrieval stack. At indexing time, a frozen document encoder $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$7 embeds each chunk $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$8 into $$\theta^*,\omega^*=\arg\min_{\theta,\omega}\frac{1}{|T|}\sum_{(x,y)\in T}\mathcal L(f_\theta(x;R_\omega),y).$$9, and the embeddings are stored in an approximate nearest-neighbor index. At query time, a trainable query encoder $k$0 produces $k$1, similarities are computed as

$k$2

and only the query encoder is fine-tuned, so the index embeddings remain valid (Zhang et al., 1 Feb 2026).

The central idea is a fine-grained variant of InfoNCE with three tiers of candidates: positives $k$3, distractors $k$4, and other negatives $k$5. After normalization,

$k$6

and the adjusted scores are

$k$7

with $k$8. For each positive chunk,

$k$9

and the loss is

$=16$0

By excluding positives from the denominator and up-weighting $=16$1 over $=16$2, LORE is designed to penalize surface-level distractors more heavily than random negatives (Zhang et al., 1 Feb 2026).

Logical structure is injected through a two-stage query rewriting pipeline. First, a subset of false chunks is sampled as $=16$3. Second, an LLM is prompted with Rhetorical Structure Theory relations such as Contrastive, Causal, and Explanatory to fuse the original query and distractor content into a single rewritten query embedding misleading facts. Third, original true chunks are mapped to $=16$4, used distractors to $=16$5, and all other false chunks to $=16$6. The resulting fine-grained dataset is termed CoEnTrain (Zhang et al., 1 Feb 2026).

The reported setup uses MS MARCO for CoEnTrain annotations, HotpotQA and MuSiQue for zero-shot transfer, Qwen3-Embedding-0.6B and BGE-M3 as embedding models, and baselines Raw, +InfoNCE, and +LORE. Training uses a single A800 GPU, $=16$7 epoch, batch size $=16$8, learning rate $=16$9, $\alpha=8$0, $\alpha=8$1, and $\alpha=8$2, with results reported as mean$\alpha=8$3std over three seeds (Zhang et al., 1 Feb 2026). On the disturbed HotpotQA test set at $\alpha=8$4 with Qwen3-Embedding, the Raw model has $\alpha=8$5 and $\alpha=8$6, +InfoNCE has $\alpha=8$7 and $\alpha=8$8, and +LORE has $\alpha=8$9 and $=0.1$0. Similar gains appear at @5 and @10, and transfer to MuSiQue and MS MARCO. The paper reports up to $=0.1$1–$=0.1$2 pp in positive recall and a $=0.1$3–$=0.1$4 pp drop in distractor retrieval relative to vanilla InfoNCE (Zhang et al., 1 Feb 2026).

LORE therefore represents an LRE whose primary function is not broader recall in general, but retrieval robustness under logical traps such as “X but not Y.” It requires no external supervision, resources, or pre-retrieval analysis, remains index-compatible, and fine-tunes only the query side (Zhang et al., 1 Feb 2026).

4. Hyperbolic metric learning for reference-report retrieval

In radiology report generation, the Learnable Retrieval Enhancer appears as a module inside REVTAF, where it retrieves semantically similar reference reports to enhance image representations, particularly for underrepresented tail-class inputs (Zhou et al., 10 Jul 2025). The input is a mini-batch of images $=0.1$5 and a large offline database of reports $=0.1$6. The submodules include entity-specific logit extraction with the MedKLIP vision encoder, a hyperbolic neural network (HNN), pairwise hyperbolic distance computation, a ground-truth semantic distance based on CheXbert status vectors, ranking-based metric learning, and reference retrieval followed by Global Reference Prompt generation (Zhou et al., 10 Jul 2025).

The vision encoder produces per-entity logits

$=0.1$7

These are mapped into a Poincaré-ball embedding

$=0.1$8

through Möbius-linear layers plus an exponential map. Pairwise distances are computed as $=0.1$9. The ground-truth semantic distance uses $w_{ij}$0-dimensional CheXbert status vectors $w_{ij}$1 and Hamming distance

$w_{ij}$2

The nearest-neighbor index for sample $w_{ij}$3 is

$w_{ij}$4

while predicted neighbor probabilities are obtained by softmax over negative hyperbolic distances,

$w_{ij}$5

Retrieval uses the score

$w_{ij}$6

and selects

$w_{ij}$7

The paper notes that if one had an additional contextual matching metric $w_{ij}$8, one could combine it with $w_{ij}$9, but in LRE as described only $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$0 is used (Zhou et al., 10 Jul 2025).

Training jointly minimizes

$$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$1

where $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$2 is cross-entropy on report tokens, $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$3 is cross-entropy between the ground-truth index $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$4 and the softmax over $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$5, and $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$6 is the Fine-grained Cross-modal Consistency loss (Zhou et al., 10 Jul 2025). During training, the retrieved reports are chosen by ground-truth semantic distance; during inference, they are chosen by learned hyperbolic distance. The retrieved report is encoded by the MedKLIP text encoder to yield Global Reference Prompts $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$7, which are fused into the visual feature map $$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$8 through Multi-Prompts Sinkhorn Attention:

$$W_j=\max_{i\in\mathrm{tokens}}\log(1+\mathrm{ReLU}(w_{ij})).$$9

followed by a residual connection and projection (Zhou et al., 10 Jul 2025).

The reported hyperparameters are $W\in\mathbb R^{|V|}$0, $W\in\mathbb R^{|V|}$1, batch size $W\in\mathbb R^{|V|}$2, ranking loss weight $W\in\mathbb R^{|V|}$3, FCC loss weight $W\in\mathbb R^{|V|}$4, temperature $W\in\mathbb R^{|V|}$5, top-1 nearest-neighbor retrieval, AdamW with learning rate $W\in\mathbb R^{|V|}$6, weight decay $W\in\mathbb R^{|V|}$7, cosine schedule, and $W\in\mathbb R^{|V|}$8 epochs. The visual encoder is ResNet-101 ImageNet pre-trained and the text encoder is MedKLIP pre-trained (Zhou et al., 10 Jul 2025). Experiments report that REVTAF achieves an average improvement of $W\in\mathbb R^{|V|}$9 on MIMIC-CXR and $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$00 on IU X-Ray, and comparisons with mainstream multimodal LLMs further highlight its superiority in radiology report generation (Zhou et al., 10 Jul 2025).

This multimodal variant shows that an LRE need not operate directly on textual retrieval scores; it can instead learn a curvature-aware retrieval metric whose output is fused back into another modality.

5. Evidence extraction as an online LRE in RAG

In RAG, an LRE may operate after retrieval rather than before it. "Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation" describes LEAR as a Learnable Retrieval Enhancer that learns to extract rational evidence from the top-$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$01 retrieved passages $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$02 (Zhao et al., 21 Jul 2025). Traditional extraction-based RAG is written as

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$03

where $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$04 is the extraction policy and $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$05 is the answer-generation LLM. LEAR replaces straightforward extraction with a rationale-then-extract policy,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$06

and trains under

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$07

A single LLM writes one long response with <reason>, <extract>, and <answer> segments. To disentangle contributions, LEAR applies knowledge-token masking: for $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$08, extracted evidence is masked; for $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$09, both passages and rationale are masked; for $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$10, no mask is applied (Zhao et al., 21 Jul 2025). The reward has three components in $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$11: answer-quality reward based on unigram $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$12, length rewards for rationale and evidence, and a format reward for well-formed tags. The final reward is a weighted average over the three masked contexts. Optimization uses Group Relative Policy Optimization, with normalized advantages

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$13

and a PPO-style clipped surrogate with a KL penalty (Zhao et al., 21 Jul 2025).

The reported experimental setting uses Natural Questions, TriviaQA, and HotpotQA, DPR over Wikipedia with top-5 retrieval, and Qwen2.5-1.5B and Qwen2.5-7B as the base LLMs. Metrics are Exact Match, unigram $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$14, and Compression Ratio (Zhao et al., 21 Jul 2025). For the 1.5B model on Natural Questions, full passages yield EM $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$15, $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$16 $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$17, and CR $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$18, whereas LEAR yields EM $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$19, $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$20 $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$21, and CR $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$22. On TriviaQA, LEAR obtains $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$23 $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$24 versus full-context $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$25 with CR $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$26; on HotpotQA, LEAR obtains $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$27 $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$28 versus $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$29 with CR $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$30 (Zhao et al., 21 Jul 2025). Under addition of up to $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$31 irrelevant passages, LEAR’s EM degrades by less than $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$32 pp, while a strong baseline loses more than $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$33 pp. Inference latency for the 1.5B model is $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$34s/query versus FilCo’s $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$35s/query (Zhao et al., 21 Jul 2025).

Here the LRE does not alter the first-stage retriever itself. Instead, it denoises retrieved content and compresses it into a smaller evidence budget. A plausible implication is that LREs can be positioned either before retrieval scoring, at retrieval scoring, or after retrieval as evidence selection.

6. Efficiency, compatibility, supervision, and terminological ambiguity

Across these systems, efficiency and deployment constraints are first-order design criteria. LORE is fully backward-compatible with existing ANN indexes because only the lightweight query encoder is fine-tuned and the document embeddings remain unchanged; training on a single A800 GPU takes a few hours and requires no extra LLM-in-the-loop at query time, only an LLM for offline query rewriting (Zhang et al., 1 Feb 2026). Mistral-SPLADE preserves compatibility with inverted indices by producing sparse vectors over the vocabulary (Doshi et al., 2024). LEAR keeps context windows small through compression ratios in the $f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$36–$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$37 range and can be served as a single LLM API call, or two calls if masks are applied on the fly (Zhao et al., 21 Jul 2025). REVTAF pre-computes and stores database embeddings offline and uses top-1 nearest-neighbor retrieval at inference (Zhou et al., 10 Jul 2025).

The supervision regimes also differ substantially. LORE relies on fine-grained supervision signals induced by offline query rewriting with RST relations and distractor assignment, but requires no external supervision or pre-retrieval analysis (Zhang et al., 1 Feb 2026). REVTAF uses ranking supervision derived from CheXbert-based semantic distance and optimizes it jointly with the task loss and Fine-grained Cross-modal Consistency loss (Zhou et al., 10 Jul 2025). LEAR uses reinforcement learning with rule-based rewards rather than direct relevance labels (Zhao et al., 21 Jul 2025). Mistral-SPLADE uses contrastive ranking with in-batch negatives and FLOPS regularization, without explicit hard negative mining or teacher distillation (Doshi et al., 2024).

A recurrent misconception is that “LRE” refers to one standardized module. The literature does not support that interpretation. The same acronym is also used for "Learned Relevance Eviction" in long-horizon agent memory, a few-kilobyte, CPU-only, language-model-free scorer that decides which units of history to retain before the future query is known (Lia et al., 18 Jun 2026). Its formulation uses an L2-regularized logistic regression,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$38

combined with a budgeted selection problem,

$f_\theta(x;R_{\omega_1},\ldots,R_{\omega_N}),$39

approximated by greedy density (Lia et al., 18 Jun 2026). Although this work does not define LRE as a Learnable Retrieval Enhancer, its inclusion is useful because it shows that the acronym is overloaded and that some papers use it for proactive memory retention rather than query-time retrieval.

Taken together, these results suggest several stable design patterns: preserve the external index whenever possible, concentrate learning on the query or evidence side when latency matters, and align the retrieval signal with the downstream failure mode—logical distractors, tail classes, noisy passages, or limited context budgets—rather than relying on surface similarity alone. Limitations are likewise domain-specific: LORE currently covers eight RST relations and notes that real-world queries may involve more complex or nested logical operators; REVTAF notes weak image-report level supervision and dependence on the anatomical hierarchy of chest radiographs; LEAR depends on reward design and masked evaluation; Mistral-SPLADE notes the absence of hard negatives or teacher distillation and describes FLOPS regularization as a blunt instrument (Zhang et al., 1 Feb 2026, Zhou et al., 10 Jul 2025, Zhao et al., 21 Jul 2025, Doshi et al., 2024).