Proxy Tuning: Methods and Applications

Updated 5 November 2025

Proxy tuning is a set of techniques that use surrogate models or proxy data to adapt large, black-box systems efficiently under resource and privacy constraints.
It employs methods like logit-level adjustments for language models, federated proxy updates, and proxy-based evaluation to enhance performance and reduce computational overhead.
Proxy tuning finds applications in active learning, hyperparameter optimization, and robust transfer, ensuring representativeness and alignment with the target outcomes.

Proxy tuning refers to a diverse set of methods in machine learning, optimization, systems, and social choice in which surrogate models, proxy data, or output-level adjustments are leveraged to approximate, guide, or accelerate adaptation of a primary target (often a large, black-box, or expensive-to-access system). Proxy tuning enables efficient, often indirect, customization, adaptation, or evaluation, frequently under constraints such as privacy, resource limitations, or partial observability.

1. Principles and Definitions

Proxy tuning encompasses several core ideas:

Surrogate optimization: Using a lighter or more tractable "proxy" (model, dataset, metric, or decision variable) to guide the adaptation of a more complex, expensive, or inaccessible target.
Black-box adaptation: Modifying the effective behavior of a black-box model by tuning accessible proxy models and transferring their effect (e.g., logit-level guidance) to the large model at inference or ensemble time.
Representation and coverage guarantees: In settings such as voting or active learning, designing proxy sets to guarantee specific levels of fidelity or representativeness to the true underlying distribution or task preference space.
Proxy-based evaluation: Leveraging proxy measures, synthetic labels, or models for cheap and scalable assessment or selection (e.g., for model selection, hyperparameter optimization, or zero/cold-start acquisition).

2. Proxy Tuning in Model Adaptation and Black-box Systems

2.1. Logit-level Proxy Tuning for LLMs

Proxy-tuning methods for LLMs such as those described in (Liu et al., 16 Jan 2024) enable efficient adaptation of a base model without access to its weights. The approach entails:

Training a small, accessible proxy model (the "expert") for the target task, with a corresponding untuned version (the "anti-expert").
At inference in the black-box LM, adjusting its output logits by the difference between the expert and anti-expert proxy logits:

$P_M(\mathbf{x} \mid x_{<t}) = \operatorname{softmax}\left[S_M(\mathbf{x} \mid x_{<t}) + S_{M^+}(\mathbf{x} \mid x_{<t}) - S_{M^-}(\mathbf{x} \mid x_{<t})\right]$

The logit delta serves as a lightweight, inference-time "steering" mechanism.

A crucial augmentation, Consistent Proxy Tuning (CPT) (He et al., 1 Jul 2024), aligns the training and inference objectives by incorporating the identical logit ensemble structure in both regimes, yielding further performance gains in both LLMs and vision-LLMs.

2.2. Proxy Tuning in Federated Learning

Methods such as FedPFT (Peng et al., 17 Apr 2024) and FedPT (Gao et al., 1 Oct 2024) introduce proxy tuning strategies to federated learning for foundation models:

Clients fine-tune or compress "proxy" sub-models (typically via layer-wise neuron pruning or by training a small LM locally).
The server builds a proxy-tuned large model by combining small model updates with the baseline large model's outputs, often followed by knowledge distillation for efficiency and privacy.
Alignment strategies (e.g., layerwise distillation, periodic neuron-level re-alignment, or difference-in-logits methods) guarantee that gradient or representation errors are bounded under theoretical metrics.

These approaches dramatically reduce communication and computational overhead on resource-limited clients and enable federated adaptation with only black-box access to the primary model.

2.3. Proxy Tuning in Multimodal Architectures

In the context of subject-driven image generation, proxy-tuning (Wu et al., 13 Mar 2025) uses diffusion models as proxy supervisors to generate synthetic datasets for training multimodal autoregressive models, resolving the poor subject fidelity observed in direct AR model fine-tuning. This method reveals a "weak-to-strong" phenomenon, whereby the student AR model can outperform the proxy supervisor in both subject fidelity and prompt adherence after training on synthetic proxy data.

3. Proxy Tuning in Efficient Learning and Hyperparameter Optimization

Proxy tuning techniques are exploited for computational efficiency in the following regimes:

Hyperparameter Optimization: Proxy data and proxy networks (Nath et al., 2021) enable efficient HPO by selecting small, information-dense data subsets (using mutual information or cross-correlation within task-relevant regions) and smaller, shallower architectures that correlate strongly with full-scale models under hyperparameter variation. This enables a 3–4x reduction in computational time in AutoML pipelines while preserving the quality of selected hyperparameters.
Active Learning: Proxy labeling and initial uncertainty estimation (Nath et al., 2022) allow for robust sample selection in the cold start regime, where no labeled data are available. Proxy-tuned models initialized on pseudo-labels provide actionable uncertainty measures, improving both annotation efficiency and downstream segmentation accuracy.

4. Proxy Tuning in Performance Prediction and Model Selection

Proxy models are central to efficient, generalizable performance estimation:

ProxyLM (Anugraha et al., 13 Jun 2024) predicts large LM performance on multilingual NLP tasks by regressing from proxy model performance (small or off-the-shelf models) and data/language features, achieving up to 37× speedup and significant RMSE reduction in performance prediction compared to baseline regression models.
Pre-training proxy metrics, both unsupervised (span corruption, k-shot tasks) and with supervised aggregation, can halve the error rate in predicting post-fine-tuning LLM outcomes relative to conventional perplexity (Zeng et al., 16 Apr 2025). Traditional perplexity fails at fine-grained model selection among equal-sized models, but supervised aggregation of diverse proxies reliably identifies the best-performing candidates.

5. Proxy Information and Robustness in Transfer Learning

Bayesian frameworks such as PROMPT (Sloman et al., 5 Nov 2024) address negative transfer in settings with unknown data sources and no labeled target outcomes:

Proxy information (e.g., human feedback, auxiliary variables) is used to infer the target task's parameters in the absence of labels.
A relevance-weighted likelihood reweights source data according to their estimated compatibility with the proxy-informed target, minimizing adverse effects from transfer.
Theoretical analyses show that the effectiveness of this proxy-based reweighting hinges on the fidelity of the relevance function, not the informativeness of the proxy variable itself.

6. Proxy Tuning in Systems, Networking, and Collective Decision Making

6.1. Systems and Protocols

In low-level systems (e.g., Miniproxy (Siracusano et al., 2016)), the tuning of proxies (e.g., through parameter selection such as initial window, buffer sizing, and rapid instantiation) is critical for optimized TCP acceleration, offering empirical improvements of 25–49% in TTFB and TTC depending on the chain length and network path.

HTTP/2 and HTTP/3 behave differently under proxy tuning: Under high loss/latency, proxy + BBR congestion control can yield a 90% performance boost for HTTP/2, while HTTP/3 performance is robust and relatively insensitive to proxy configuration, owing to its protocol design (Liu et al., 24 Sep 2024).

Proxy tuning methods determine the minimal set and configuration of proxies required to ensure a desired representativeness in voting systems (Anshelevich et al., 2020):

The existence and construction of $\theta$ -representative proxy sets are established via dynamic programming, with tight upper and lower bounds ( $2\lfloor 1/\theta\rfloor$ for restricted, up to $1.5\lceil 1/\theta\rceil$ for unrestricted proxies).
Under strict-Condorcet rules, a $\theta$ -representative arrangement ensures that the outcome under proxy voting is within distance $\theta$ (in candidate space) of the direct voting outcome.
These methods are directly algorithmic and computable in polynomial time, enabling fair and efficient design of voting systems.

Furthermore, proxy voting in metric spaces consistently reduces estimator error (quadratically for medians and means on intervals), remains robust under strategic participation, and is most beneficial in small or diverse samples and multi-issue settings (Cohensius et al., 2016).

7. Proxy Tuning for Robustness and Attack Transferability

In adversarial alignment and robustness, local proxy fine-tuning (LoFT) (Shah et al., 2023) targets the lexico-semantic neighborhood of harmful queries, enabling substantial gains in attack transferability to closed-source LLMs (e.g., improvement in human-evaluated attack success rates from 4.87% to 43.6% on ChatGPT). FitB (Fill-in-the-Blank) style sampling is the most effective method for collecting local fine-tuning data. This localized proxy tuning exposes vulnerabilities in even tightly aligned models and highlights the limitations of automated filter-based evaluation.

Table: Key Algorithmic Approaches in Proxy Tuning

Domain	Proxy/Surrogate Type	Tuning/Transfer Mechanism
LLMs (black-box adaptation)	Small tuned proxy LM	Logit difference added to large LM outputs
Vision/language foundation	Submodel (layerwise)	Federated distillation/alignment
Model selection/performance	Small/ensemble proxies	Supervised/unsupervised regression
Active learning, HPO	Proxy data/networks	Informativeness-based selection, downsizing
Voting/representation	Proxy arrangement	Dynamic programming, bisector alignment
Adversarial attack transfer	Local proxy fine-tuning	Neighborhood sampling + gradient search

Proxy tuning serves as a unifying paradigm for adaptive, efficient, and robust methods in scenarios where full access to data, models, or computational resources is restricted. Its effectiveness depends critically on the design of proxy selection, the fidelity of the transfer or steering mechanism, and the alignment between proxy and target—each of which is addressed with rigorous algorithmic and theoretical guarantees in contemporary research.