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ARXIV:2605.14773 · LLM TRAINING OPTIMIZATION · SUBMITTED 15 MAY · 20:12 UTC · FRESHNESS FRESH
ARXIV:2605.14773LLM TRAINING OPTIMIZATIONSUBMITTED 15 MAY · 20:12 UTCFRESHNESS FRESHSuorong Yang · Hanqi Zhu · Hai Gan · Fangjian Su · Guang Li · Furao Shen · +1 at arXiv
A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models.
Opportunity summary
Pain A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically…
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we revisit data selection from an optimization perspective and show that selected-data training induces an implicit regularization effect modulated by the…
LLM Training Optimization moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models.
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10.48550/arXiv.2605.14773A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models.
Abstract
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as the target ratio throughout training. Thus, they are often dynamic in sample identity but static in data volume. In this work, we revisit data selection from an optimization perspective and show that selected-data training induces an implicit regularization effect modulated by the instantaneous selection ratio. This reveals a key trade-off: lower ratios amplify selection-induced regularization, whereas higher ratios preserve data coverage and optimization fidelity. Motivated by this insight, we propose PODS, a Plug-and-play Oscillatory Data-volume Scheduling framework. Rather than introducing another sample-scoring metric, PODS serves as a lightweight module that dynamically schedules how much data to select over training. Under the target selection ratio, PODS alternates between low-ratio regularization phases and high-ratio recovery phases to exploit selection-induced regularization without sacrificing optimization stability. With its lightweight, ratio-level, and task-agnostic design, PODS is compatible with existing static and dynamic selection methods and broadly applicable across training paradigms. Experiments across various datasets, architectures, and tasks show that PODS consistently improves the efficiency-generalization trade-off, e.g., reducing ImageNet-1k training cost by 50% with improved accuracy and accelerating LLM instruction tuning by over 2x without performance degradation.
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PROBLEM
A plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing...
METHOD
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as t...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we revisit data selection from an optimization perspective and show that selected-data training induces an implicit regularization effect modulated by the instantaneous selection ratio. Code...
WHY NOW
LLM Training Optimization 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 plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as the target ratio throughout training.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as the target ratio throughout training.
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. In this work, we revisit data selection from an optimization perspective and show that selected-data training induces an implicit regularization effect modulated by the instantaneous selection ratio. 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
LLM Training Optimization 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 plug-and-play framework that oscillates data volume during training to improve efficiency-generalization trade-offs for LLMs and other models.
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LLM Training Optimization
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