Zero-Shot Optimization Overview

• Zero-shot optimization is a paradigm where models solve unseen tasks by using transferable representations, eliminating the need for task-specific fine-tuning.
• It leverages meta-learning, representation learning, and combinatorial design to consistently achieve robust performance across various applications.
• Empirical results on benchmarks like BBOB, ImageNet, and specialized RL tasks demonstrate improved sample efficiency and reduced computational overhead.

Zero-shot Optimization is the development and deployment of algorithmic strategies or models that can solve previously unseen optimization problems or adapt to previously unseen domains without any task-specific fine-tuning, retraining, or access to label information during test-time adaptation. This paradigm has broad implications across black-box optimization, reinforcement learning, hyperparameter selection, prompt engineering for LLMs, network resource management, domain adaptation, and zero-shot learning scenarios. In zero-shot optimization, the core challenge is to construct transferable representations, policies, or mechanisms that enable immediate, robust adaptation to a diverse set of new tasks, often with formal guarantees or empirical evidence of competitive or superior performance to classical, problem-specific baselines.

1. Foundational Problem Setting and Categories

Zero-shot optimization formalizes the requirement that a system, after exposure only to a set of source tasks, source domains, or pretraining distributions, must provide an optimal (or near-optimal) solution on a new, previously unseen target problem instance with no additional per-instance training—possibly subject to resource, data, or time constraints.

Key categories and typical formalizations include:

• Black-box optimization: Given a costly, derivative-free function $f : \mathbb{R}^d \rightarrow \mathbb{R}$, a zero-shot optimizer must propose query/evaluation strategies that achieve minimal regret on new $f$, sampled from a problem distribution, without per-task hyperparameter tuning or adaptation (Meindl et al., 3 Oct 2025, Li et al., 2024).
• Reinforcement learning (RL): An agent trains on a class of MDPs (with shared dynamics but varying reward) to construct a policy or representation that enables instant deployment (no further RL or planning) to arbitrary new reward functions (Ventura et al., 23 Oct 2025, Ollivier, 15 Feb 2025).
• Predictive optimization under zero-shot domain adaptation: Instead of predicting in a new domain, the task is to find a domain description (attribute vector) that yields a desired outcome under a learned predictive model, subject to constraints, without training in the target domain (Sakai et al., 2021).
• Hyperparameter optimization: A small set of hyperparameter configurations is pre-selected (zero-shot portfolio) to ensure that, for any new task, at least one configuration performs well, given only prior meta-data (Winkelmolen et al., 2020).
• Instruction and prompt optimization: Task instructions or prompts are optimized offline or via black-box search to enhance zero-shot inference on unseen downstream tasks, particularly in language and multimodal models (Cho et al., 2023, Zhu et al., 2024, Fang et al., 18 Mar 2025).
• Online label/proxy optimization: Sequential, streaming protocols update internal representations or proxies online as new data arrives, capturing evolving distributions while operating in the zero-shot regime (no labels, no storage) (Qian et al., 2024).

2. Methodological Foundations

Zero-shot optimization strategies rely on mechanisms that enable transfer and adaptability without explicit target-specific adaptation:

• Meta-learning and pretraining: Large-scale offline RL, learning from optimization trajectories, or reward-free exploration over diverse problem instances is used to instantiate models or policies that generalize across tasks (Meindl et al., 3 Oct 2025, Li et al., 2024, Ollivier, 15 Feb 2025).
• Representation learning: Universal or reward-parameterized value functions, successor features, attribute prototypes, and other latent representations are constructed to decouple environment dynamics/semantics from task-specific details (Ventura et al., 23 Oct 2025, Du et al., 2022, Wang et al., 2019).
• Combinatorial design and submodular optimization: Zero-shot portfolios (e.g., for hyperparameter tuning) are built using greedy selection under supermodular meta-losses to maximize coverage/diversity (Winkelmolen et al., 2020).
• Constrained discrete optimization: Prompt search for LLMs exploits constrained generation and beam search over the prompt space, with metrics computed on small sets of held-out tasks, all without gradient-based updates (Cho et al., 2023).
• Dual-based analytic adaptation: In dynamic systems (e.g., network optimization), Taylor expansions of complementary slackness yield analytic, instant updates for dual variables after abrupt environment changes ("zero-shot Lagrangian update") (Hou, 2024).
• Sequential online optimization: Dual methods and online proxies are employed to continuously update internal class proxies and label distributions, maintaining zero-shot classification performance on streaming, unlabeled input (Qian et al., 2024).

3. Algorithmic and Model Architectures

A diversity of architectures and algorithmic blueprints underpin zero-shot optimization:

Area	Typical Model or Mechanism	Notable Example(s)
Black-box optimization	Pretrained decision-transformer policy, meta-learned DE	ZeroShotOpt (Meindl et al., 3 Oct 2025), GPOM (Li et al., 2024)
RL	Universal value function or successor features	USF, SF-Laplacian (Ventura et al., 23 Oct 2025)
Hyperparameter optimization	Greedy/surrogate submodular selection, HyperBand	OBO, MF (Winkelmolen et al., 2020)
Prompt/instruction search	Constrained beam search, ICL-based rewrites	Co-Prompt (Cho et al., 2023), VisLingInstruct (Zhu et al., 2024), PLAY2PROMPT (Fang et al., 18 Mar 2025)
Domain attribute search	Convex/Semidefinite programming	ZSDA predictive opt (Sakai et al., 2021)
Online streaming labeling	Online dual/KKT, gradient-projection in label/proxy space	OnZeta (Qian et al., 2024)
Dual-based network control	Analytic Lagrangian update via Taylor expansion	Zero-Shot Lagrangian Update (Hou, 2024)

Pretraining and Meta-Optimization

• Offline pretraining often leverages >10⁶ synthetic tasks (e.g., GP-sampled functions) and ensemble expert trajectories to induce transferable policies or update rules (e.g., transformer-based ZeroShotOpt (Meindl et al., 3 Oct 2025), population-based GPOM (Li et al., 2024)).
• For RL, both policy and feature learning leverage reward-free or reward-agnostic objectives (e.g., mutual information, white-noise reward prior), supporting transfer to arbitrary reward functions (Ollivier, 15 Feb 2025, Ventura et al., 23 Oct 2025).

Proxy and Label Space Optimization

• Online zero-shot learning augments CLIP-style models with sequential dual updates and proxy gradient steps to balance class frequencies and bridge vision-text modalities, all under formal regret bounds (Qian et al., 2024).

Constrained and Combinatorial Search

• For prompt/instruction optimization, beam search guided by discriminator metrics enables zero-shot prompt discovery without LLM parameter updates (Cho et al., 2023, Fang et al., 18 Mar 2025).
• For hyperparameter selection, submodular greedy algorithms and multi-fidelity resource allocation (HyperBand) yield cover sets that guarantee good worst-case performance for unseen datasets (Winkelmolen et al., 2020).

4. Empirical Performance and Theoretical Guarantees

Zero-shot optimization strategies consistently demonstrate competitive or superior performance to hand-tuned, problem-specific baselines in both sample efficiency and wall-clock inference time. Notable results include:

• Black-box optimization: ZeroShotOpt matches/exceeds 12 hand-tuned BO variants across GP/BBOB/VLSE/HPO-B benchmarks (normalized performance P~0.647–0.885). GPOM achieves top average rank on BBOB suite for d∈{30,100,500}, outperforming CMA-ES, L-SHADE, and meta-learned ES/GA (Meindl et al., 3 Oct 2025, Li et al., 2024).
• RL: Extended error bounds for successor-feature methods provide explicit decompositions over representation, linearization, and inference errors (Ventura et al., 23 Oct 2025); direct minimization of the zero-shot RL loss for various priors (white-noise, Dirichlet, scattered) enables new algorithmic perspectives and theoretical insights (Ollivier, 15 Feb 2025).
• Prompt/instruction optimization: Co-Prompt and PLAY2PROMPT yield up to +6.7pp gains in zero-shot re-ranking and tool use, respectively, even under incomplete or noisy documentation (Cho et al., 2023, Fang et al., 18 Mar 2025); VisLingInstruct achieves +13.1% on TextVQA and +9.0% AUC on HatefulMemes over prior state-of-the-art (Zhu et al., 2024).
• Hyperparameter optimization: Surrogate- and multi-fidelity-based zero-shot portfolios achieve up to 9% improvement in relative error difference (RED) compared to random portfolios, with orders-of-magnitude less computation (Winkelmolen et al., 2020).
• Online streaming: OnZeta achieves 78.94% accuracy on ImageNet (ViT-L/14@336), outperforming standard CLIP zero-shot and approaching offline upper bounds despite never revisiting data (Qian et al., 2024).
• Network adaptation: Zero-shot Lagrangian updates enable convergence to new optima within one iteration, reducing transient violation and utility gaps by orders-of-magnitude compared to classic dual-based adaptation (Hou, 2024).
• Predictive optimization: Attribute-optimized design in ZSDA outperforms feature-unaware baselines and achieves efficient approximation guarantees under both convex and binary-interaction formulations (Sakai et al., 2021).

5. Domain-Specific Challenges and Research Directions

Zero-shot optimization exposes several open research directions and foundational questions:

• Representation expressiveness: Trade-offs between linear, reward-agnostic, or task-conditioned representations, and the impact on extrapolation and error bounds, remain an active area, particularly beyond linear reward assumptions in RL (Ventura et al., 23 Oct 2025, Ollivier, 15 Feb 2025).
• Task distribution and robustness: Meta-training on synthetic or simplified distributions may not capture the complexity of real-world tasks. Generalization to high-dimensional, highly structured, or non-iid settings is a persistent challenge (Meindl et al., 3 Oct 2025, Li et al., 2024).
• Supervision and annotation cost: Fine-grained preference optimization demonstrates dramatic data-efficiency gains (×4 less data for TTS), but segment-level annotation remains expensive (Yao et al., 5 Feb 2025).
• Scalability and computational efficiency: While meta-learned and beam search-based approaches are competitive, scaling to large parameter spaces, long-horizon inference, or huge class/attribute spaces is nontrivial.
• Combinatorial and interactive domains: Extending prompt or tool optimization to multi-step, interactive scenarios, and compositional or multi-tool chains is largely unexplored (Fang et al., 18 Mar 2025).
• Theoretical frameworks: Clear taxonomies, such as the direct vs. compositional value-function frameworks in zero-shot RL, are needed to unify progress, benchmark progress, and guide development (Ventura et al., 23 Oct 2025).
• End-to-end vs. two-stage pipelines: Joint optimization of predictive and decision architectures (e.g., in ZSDA or meta-learned optimizers) may yield further improvements (Sakai et al., 2021, Li et al., 2024).

6. Representative Benchmarks and Tasks

Broad empirical validation has been performed across:

• Black-Box Optimization: GP synthetic benchmarks, BBOB, VLSE, and HPO-B (hyperparameter tasks) (Meindl et al., 3 Oct 2025, Li et al., 2024).
• Reinforcement Learning: Unsupervised skill discovery, reward transfer, transfer to dense/sparse/temporal reward MDPs (Ventura et al., 23 Oct 2025, Ollivier, 15 Feb 2025).
• Domain Adaptation/Design: Multi-domain regression/classification (Sushi, Coffee, Book), attribute-based design (Sakai et al., 2021).
• Prompt/Instruction Optimization: MS-MARCO, Natural Questions, StableToolBench, Function-Calling Leaderboard (Cho et al., 2023, Fang et al., 18 Mar 2025, Zhu et al., 2024).
• Online/Streaming Classification: ImageNet and 13 diverse vision benchmarks (e.g., Aircraft, Caltech101, UCF101) under streaming/online evaluation settings (Qian et al., 2024).
• Supervised and Contrastive ZSL: CUB, SUN, AwA2 for visual attribute embedding (Du et al., 2022, Wang et al., 2019).
• Network Control: Dynamic user/resource/channel settings in multi-user utility maximization and stochastic service scheduling (Hou, 2024).

7. Synthesis and Broader Impacts

Zero-shot optimization, as a cross-cutting principle, bridges meta-learning, transfer learning, unsupervised representation learning, and combinatorial design. Its emergent architectures—pretrained decision transformers, population-parameterized meta-optimizers, reward-free RL, submodular portfolio construction, instruction/prompt search, and analytic dual adjustment—demonstrate the feasibility of reliable transfer without explicit adaptation. This paradigm is reshaping best practices in black-box optimization, real-time adaptation, AI alignment, policy transfer, automated ML, network control, and multimodal learning, with scalability and robust generalization as enduring priorities for future research (Meindl et al., 3 Oct 2025, Ventura et al., 23 Oct 2025, Li et al., 2024, Hou, 2024, Winkelmolen et al., 2020).