Prompt Overfitting in Pre-trained Models

• Prompt overfitting is a phenomenon where learnable prompts become overly tuned to specific training data patterns, reducing the ability to generalize across tasks.
• It manifests as improved performance on training classes while causing a consistent decline on unseen classes due to shifts in gradient directions towards spurious features.
• Mitigation strategies such as gradient constraint, subspace projection, and meta-learning regularization help preserve general, domain-agnostic knowledge during adaptation.

Prompt overfitting refers to the phenomenon wherein learnable prompts, used to adapt large pre-trained models such as vision-LLMs (VLMs) for downstream tasks, become overly specialized to the training data—compromising generalization to unseen classes, domains, or variations in prompt formulation. This issue is especially acute under few-shot or limited-data conditions, where the adaptation “forgets” the broad, domain-agnostic knowledge acquired during pretraining in favor of spurious, task-specific associations. Prompt overfitting can occur in both textual and visual prompt learning as well as in reinforcement learning settings where the prompt structure itself is treated as an input to the decision policy.

1. Mechanisms and Manifestations of Prompt Overfitting

Prompt overfitting emerges when the tuned prompt preferentially exploits specific patterns or noise present in a small labeled set, at the expense of retaining the general knowledge encoded by the underlying pre-trained model. In standard prompt tuning methods such as CoOp, this can lead to two characteristic effects (Ma et al., 2022):

• Performance on base (training) classes initially increases but eventually degrades with further tuning.
• Performance on novel (unseen) classes consistently decreases, sometimes falling below that of the original manually designed (“zero-shot”) prompt.

This is explained by a shift in the gradient flow during training: early updates track directions aligned with generalizable features, whereas extended tuning shifts the prompt towards spurious features with limited generalizability. Similar behaviors have been documented in both textual prompt learning for factual knowledge extraction and in visual prompting, where an increase in prompt parameter count without proper regularization leads to improved training but reduced test accuracy (Enomoto, 9 Oct 2025).

Prompt overfitting is not limited to parameter-efficient vision-language adaptation. In RL-finetuned LLMs, sensitivity to prompt formulation can dramatically degrade an agent’s performance when faced with unseen prompt templates, as revealed by drops as large as 30% in success rate when tested on alternative formulations compared to those encountered in training (Aissi et al., 25 Oct 2024).

2. Techniques for Diagnosing and Quantifying Overfitting

A rigorous understanding of prompt overfitting can be developed through several complementary analyses:

• Gradient Flow Analysis: Principal component analysis (PCA) of the trajectory of prompt embeddings during training demonstrates that generalization degradation correlates with shifts in the dominant gradient directions, which become nearly orthogonal to their early-stage (generalizable) counterparts (Ma et al., 2022).
• Representational Studies: Visualization tools such as UMAP applied to internal latent state representations confirm that prompt-tuned models exhibit clustering by prompt template rather than by semantic content when overfitting occurs (Aissi et al., 25 Oct 2024).
• Generalization Bounds: Theoretical analysis via PAC-Bayes theory demonstrates that discrete prompt engineering with small hypothesis spaces is less susceptible to classical overfitting, particularly when coupled with a LLM prior to constrain prompt naturalness (Akinwande et al., 2023).
• Prompt Bias Quantification: In LLMs, Jensen–Shannon divergence between output distributions under “prompt-only” (no subject) queries and uniform distributions serves as an indicator of prompt-driven overfitting to specific answers (Xu et al., 15 Mar 2024).

3. Algorithmic Mitigation Strategies

Numerous approaches have been developed to prevent or reduce prompt overfitting:

Gradient Constraint and Projection

Methods such as ProGrad (Zhu et al., 2022) restrict prompt parameter updates by aligning the gradient from the downstream task (cross-entropy loss, $G_{ce}$) with a “general direction” derived from the gradient of the Kullback–Leibler (KL) loss between predictions from the current prompt and a fixed, hand-crafted zero-shot prompt ($G_{kl}$). The update is:

$$G = \begin{cases} G_{ce} & \text{if } G_{ce} \cdot G_{kl} \geq 0 \ G_{ce} - \lambda \frac{G_{ce} \cdot G_{kl}}{||G_{kl}||^2} G_{kl} & \text{otherwise} \end{cases}$$

This ensures that updates never contradict the encyclopedic knowledge of the pre-trained VLM.

Subspace Projection

Subspace Prompt Tuning (SubPT) (Ma et al., 2022) fixes prompt updates to the subspace spanned by early-stage gradient directions, identified via PCA:

$v \leftarrow v - \alpha U^TU \nabla_v L_{ce}(v)$

where $U$ contains the eigenvectors from the early, generalizable training period.

Meta-Learned Regularization

ProMetaR (Park et al., 1 Apr 2024) meta-learns both the prompt regularizer and the prompt via a bi-level optimization. The regularizer adapts the weight of penalty terms to control the magnitude and alignment of prompt update directions, achieving a balance between task adaptation and the preservation of task-agnostic knowledge.

Prompt Mixtures and Gating

Mixture-of-prompts approaches (Du et al., 18 Sep 2024) assign multiple “expert” soft prompts and use a dynamically learned router (gating network)—guided by similarity to grouped hard prompt templates and KL-regularized—to encourage the selection of a blend of prompts tuned for style diversity without forgetting the general prior.

4. Regularization and Data Strategies

Practical regularization further reduces prompt overfitting:

• Task-augmented meta-learning: Generating virtual tasks (e.g., via manifold mixup) for meta-regularization combats “meta-overfitting” when the validation set is small (Park et al., 1 Apr 2024).
• Perplexity-based Regularization: PLPP computes the perplexity of the prompt from a LLM head and adds a self-distillation loss between cosine-similarity-derived soft labels and the prompt’s output word distribution. Mutual self-distillation between perplexity and inverted perplexity loss provides further regularization (Liu et al., 18 Dec 2024).
• Entropy Constraints: ProAPO (Qu et al., 27 Feb 2025) employs a composite fitness score $F(\mathcal{D}, P) = Acc + \alpha H$, where $H = \mathbb{E}_{x,y}[-\log s(x, y)]$ is an entropy term over the predicted (soft) class probabilities, penalizing overconfident, overfitted prompts.
• Data Augmentation: In visual prompting, randomized augmentations (e.g., TrivialAugment) are highly effective at mitigating overfitting to prompt patterns, outperforming explicit regularizers such as weight decay or dropout (Enomoto, 9 Oct 2025).

5. Robustness, Generalization, and Empirical Results

Across settings, effective prompt overfitting mitigation translates to:

• Robust base-to-novel generalization: Techniques such as gradient projection, meta-regularization, and prompt diffusion consistently increase harmonic mean accuracy across base and novel classes on standard benchmarks (Zhu et al., 2022, Park et al., 1 Apr 2024, Du et al., 26 Oct 2024).
• Resilience under domain shift: Approaches that explicitly diversify prompt responses (e.g., by synthesizing style shifts (Talemi et al., 25 Nov 2024)) or employ cross-modal alignment (e.g., with Maximum Mean Discrepancy (Sun et al., 22 Jul 2024)) reliably outperform fixed or naively fine-tuned prompts under cross-dataset and domain generalization settings.
• Reduced sensitivity to prompt template: In RL-finetuned LLM agents, contrastive regularization of latent representations ensures that policy performance remains stable across various prompt templates, avoiding degradation when the prompt syntax differs from the one used in training (Aissi et al., 25 Oct 2024).

Table: Strategies to Mitigate Prompt Overfitting | Method/Class | Technical Principle | Generalization Effect | |---------------------- | -------------------------------- | ------------------------------------| | ProGrad (Zhu et al., 2022) | KL-aligned gradient projection | Maintains VLM general knowledge | | SubPT (Ma et al., 2022) | Project onto early eigensubspace | Prevents drift to spurious features | | ProMetaR (Park et al., 1 Apr 2024) | Meta-learn prompt regularizer | Adaptive, task-agnostic retention | | Mixture (Du et al., 18 Sep 2024) | Prompt mixture w/ gating loss | Avoids overfitting to single style | | ACAVP (Enomoto, 9 Oct 2025) | Augment input transformations | Improves robustness, generalization | | PLPP (Liu et al., 18 Dec 2024) | Perplexity/self-distillation | Promotes semantic regularization |

Empirical results demonstrate:

• Improvements of several percentage points in harmonic mean test accuracy on challenging few-shot and domain transfer tasks for methods employing gradient projection or meta-regularization (Zhu et al., 2022, Ma et al., 2022, Park et al., 1 Apr 2024).
• Performance stability and reduced overfitting metrics are consistently recorded with entropy regularization or hybrid prompt mixture systems (Qu et al., 27 Feb 2025, Du et al., 18 Sep 2024).

6. Notable Limitations and Open Challenges

Despite progress, some prompts remain susceptible to overfitting:

• Prompt bias: Even ostensibly general prompts can lead to overfitted responses, especially with imbalanced data (Xu et al., 15 Mar 2024).
• Explosion in search space: Automated class-specific prompt generation, especially when driven by LLMs, increases the risk of overfitting unless guided by entropy or regularization constraints (Qu et al., 27 Feb 2025).
• RL environments: Alignment between prompt formulation and downstream latent representations is not guaranteed by standard RL objectives; additional contrastive objectives are required (Aissi et al., 25 Oct 2024).

Further, strategies such as advanced meta-regularization, bi-level optimization (e.g., separate data splits for parameter and prompt updates in BLO-SAM (Zhang et al., 26 Feb 2024)), or Bayesian learning with explicit priors over logits (Kim et al., 19 Apr 2025) continue to be active areas of research to address the overfitting-generalization tradeoff.

7. Implications and Future Directions

The prevalence of prompt overfitting highlights the importance of principled adaptation techniques for large, pre-trained models. General trends emerging from recent works include:

• Guided prompt updates grounded by either pre-trained knowledge (via gradient, subspace, or probabilistic priors) or meta-regularization.
• Architectural strategies that separate the adaptation of prompt/task-specific information from domain-general guidance, such as multi-stage or cascaded prompting (Wu et al., 26 Sep 2024).
• Explicit augmentation and regularization, both at the input level (augmentation) and in feature/logit space (distillation, entropy, consistency constraints), as universal overfitting mitigation tools.

As prompt learning continues to gain traction across modalities and tasks, the understanding and resolution of prompt overfitting will remain central to achieving robust, generalizable, and efficient model adaptation.