Test-Time Meta-Adaptation with Self-Synthesis

• URL: http://arxiv.org/abs/2603.03524v1
• Date: Tue, 03 Mar 2026 21:16:18 GMT
• Title: Test-Time Meta-Adaptation with Self-Synthesis
• Authors: Zeyneb N. Kaya, Nick Rui,
• Abstract summary: We introduce MASS, a meta-learning framework that enables large language models to self-adapt.<n> MASS generates problem-specific synthetic training data and performs targeted self-updates optimized for downstream performance.<n> Experiments on mathematical reasoning show that MASS learns to synthesize per-instance curricula that yield effective, data-efficient test-time adaptation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As strong general reasoners, large language models (LLMs) encounter diverse domains and tasks, where the ability to adapt and self-improve at test time is valuable. We introduce MASS, a meta-learning framework that enables LLMs to self-adapt by generating problem-specific synthetic training data and performing targeted self-updates optimized for downstream performance at inference time. We train this behavior end-to-end via bilevel optimization: an inner loop adapts on self-generated examples while an outer loop meta-learns data-attribution signals and rewards post-update task performance. The synthetic data is optimized with scalable meta-gradients, backpropagating the downstream loss through the inner updates to reward useful generations. Experiments on mathematical reasoning show that MASS learns to synthesize per-instance curricula that yield effective, data-efficient test-time adaptation.

Related papers

• Meta-learning to Address Data Shift in Time Series Classification [0.0]
  Traditional deep learning (TDL) models perform well when training and test data share the same distribution.<n>The dynamic nature of real-world data renders TDL models prone to rapid performance degradation, requiring costly relabeling and inefficient retraining.<n>Here, we systematically compare TDL with fine-tuning and optimization-based meta-learning algorithms to assess their ability to address data shift.
  arXiv  Detail & Related papers  (2026-01-13T22:38:43Z)
• SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation [3.2150327776278576]
  This paper introduces the Synthetic dataset Quality Metric (SDQM) to assess data quality for object detection tasks.<n>In our experiments, SDQM demonstrated a strong correlation with the mean Average Precision (mAP) scores of YOLOv11, a leading object detection model.<n>It provides actionable insights for improving dataset quality, minimizing the need for costly iterative training.
  arXiv  Detail & Related papers  (2025-10-08T03:01:26Z)
• Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning [44.53583316198435]
  Supervised Fine-Tuning (SFT) Large Language Models rely on high-quality training data.<n>We introduce Middo, a self-evolving Model-informed dynamic data optimization framework.<n>We show that Middo consistently enhances the quality of seed data and boosts LLM's performance with improving accuracy by 7.15% on average.
  arXiv  Detail & Related papers  (2025-08-29T12:47:27Z)
• CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks [59.69339605157168]
  CoT-Self-Instruct is a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought.<n>In verifiable reasoning, our synthetic data significantly outperforms existing training datasets.<n>For non-verifiable instruction-following tasks, our method surpasses the performance of both human and standard Self-Instruct training data.
  arXiv  Detail & Related papers  (2025-07-31T17:38:50Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.<n>However, improvement is plateauing due to the exhaustion of readily available high-quality data.<n>We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks [40.91931801667421]
  Our paper presents a novel global-to-local algorithm that interleaves influence function as a data selection method with Bayesian optimization to optimize data mixture via feedback from a specific unseen evaluation task.<n>By analyzing DUET's cumulative regret, we theoretically show that DUET converges to the optimal training data mixture for an unseen task even without any data knowledge of the task.
  arXiv  Detail & Related papers  (2025-02-01T01:52:32Z)
• TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use [72.32614703504122]
  Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments.<n>Standard supervised fine-tuning approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use.<n>We propose TL-Training, a task-feature-based framework that mitigates the effects of suboptimal training data.
  arXiv  Detail & Related papers  (2024-12-20T02:21:36Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models [39.347666307218006]
  Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across diverse applications.<n>We introduce EPIC, a novel approach that leverages balanced, grouped data samples and consistent formatting with unique variable mapping to guide LLMs in generating accurate synthetic data across all classes, even for imbalanced datasets.
  arXiv  Detail & Related papers  (2024-04-15T17:49:16Z)
• Noisy Self-Training with Synthetic Queries for Dense Retrieval [49.49928764695172]
  We introduce a novel noisy self-training framework combined with synthetic queries. Experimental results show that our method improves consistently over existing methods. Our method is data efficient and outperforms competitive baselines.
  arXiv  Detail & Related papers  (2023-11-27T06:19:50Z)
• Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding [38.11411155621616]
  We study self-training as one of the predominant semi-supervised learning approaches. We present UPET, a novel Uncertainty-aware self-Training framework. We show that UPET achieves a substantial improvement in terms of performance and efficiency.
  arXiv  Detail & Related papers  (2023-10-19T02:18:29Z)
• MLLM-DataEngine: An Iterative Refinement Approach for MLLM [62.30753425449056]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data. For quality, we resort to GPT-4 to generate high-quality data with each given data type.
  arXiv  Detail & Related papers  (2023-08-25T01:41:04Z)
• Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning [117.48444197402858]
  We propose ePisode cUrriculum inveRsion (ECI) during data-free meta training and invErsion calibRation following inner loop (ICFIL) during meta testing.<n>ECI adaptively increases the difficulty level of pseudo episodes according to the real-time feedback of the meta model.<n>We formulate the optimization process of meta training with ECI as an adversarial form in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-03-20T15:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.