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ARXIV:2605.13149 · DATA GENERATION · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13149DATA GENERATIONSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHIshika Agarwal · Sofia Stoica · Emre Can Acikgoz · Pradeep Natarajan · Mahdi Namazifar · Jiaqi Ma · +1 at arXiv
A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness.
Opportunity summary
Pain A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness.
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A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness. Researchers have explored many ways to generate top quality samples.
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our experimental results indicate that (1) student models trained with AcquisitionSynthesis data achieve good performance on in-distribution tasks (2-7% gain) and is more robust…
Data Generation moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness.
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10.48550/arXiv.2605.13149A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness.
Abstract
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples. Some works rely on rejection sampling: generating lots of synthetic samples and filtering out low-quality samples. Other works rely on larger or closed-source models to extract model weaknesses, necessary skills, or a curriculum off of which to base data generation. These works have one common limitation: there is no quantitative approach to measure the impact of the generated samples on the downstream learner. Active learning literature provides exactly this, in the form of acquisition functions. Acquisition functions measure the informativeness and/or influence of data, providing interpretable, model-centric signals. Inspired by this, we propose AcquisitionSynthesis: using acquisition functions as reward models to train language models to generate higher-quality synthetic data. We conduct experiments on classic verifiable tasks of math, medical question-answering, and coding. Our experimental results indicate that (1) student models trained with AcquisitionSynthesis data achieve good performance on in-distribution tasks (2-7% gain) and is more robust to catastrophic forgetting, and (2) AcquisitionSynthesis models can generate data for other models and for low-to-high resource training paradigms. By leveraging acquisition rewards, we seek to demonstrate a principled path toward model-aware self-improvement that surpasses static datasets.
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PROBLEM
A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness. Researchers have explored many ways to generate top quality samples.
METHOD
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our experimental results indicate that (1) student models trained with AcquisitionSynthesis data achieve good performance on in-distribution tasks (2-7% gain) and is more robust to catastrophic forgetting...
WHY NOW
Data Generation moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness. Researchers have explored many ways to generate top quality samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our experimental results indicate that (1) student models trained with AcquisitionSynthesis data achieve good performance on in-distribution tasks (2-7% gain) and is more robust to catastrophic forgetting, and (2) AcquisitionSynthesis models can generate data for other models and for low-to-high resource training paradigms. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Data Generation moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel approach to synthetic data generation that uses acquisition functions to train language models, leading to improved downstream model performance and robustness.
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