Demystifying Data Organization for Enhanced LLM Training
Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li
Abstract
Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely studied, the strategic data organization for enhanced training remains an underexplored area, particularly since current LLMs are often trained for only one or a few epochs. This paper systematically explores the influence of data organization on LLM training by reusing pre-computed sample-level scores originally generated for data efficiency, thereby incurring minimal additional computational overhead. We identify and formalize four key guidelines for optimizing data organization: Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, and Local Diversity. Guided by them, we introduce two novel data ordering methods termed STR and SAW. Extensive experiments across different model scales and data sizes, encompassing both pre-training and SFT stages, validate the effectiveness of our summarized guidelines. They also demonstrate the robustness of our proposed data ordering methods in enhancing the stability and performance of LLM training. Github Link: https://github.com/microsoft/data-efficacy/
AI Impact Assessments
(1 models)Scientific Impact Assessment
1. Core Contribution
This paper addresses data organization (ordering) for LLM training—a relatively underexplored dimension compared to data selection, filtering, and mixing. The key insight is that pre-computed sample-level scores (e.g., educational quality scores from FineWeb-Edu) already exist for data selection purposes and can be repurposed at near-zero cost to strategically order training data. The authors formalize four guidelines: Boundary Sharpening (control start/end data characteristics), Cyclic Scheduling (periodic revisitation across score ranges), Curriculum Continuity (smooth transitions between data distributions), and Local Diversity (heterogeneity within mini-batches). These are instantiated as modular algorithmic components (SEG, FO, ZIG, JIT) and combined into two cross-guidance strategies: STR and SAW.
The core novelty lies not in any single ordering technique—curriculum learning, folding, and jittering have precedents—but in their systematic decomposition and principled composition. The paper provides the first structured framework for reasoning about data ordering in LLM training, moving from ad-hoc heuristics to formalized guidelines.
2. Methodological Rigor
Strengths in experimental design:
Weaknesses:
3. Potential Impact
Practical impact: The approach is immediately applicable to any LLM training pipeline that already computes sample-level scores. The near-zero additional cost makes adoption easy. The open-source release enhances reproducibility.
Broader influence: This work could catalyze a subfield of "training data scheduling" that goes beyond simple curriculum learning. The decomposition into orthogonal guidelines provides a vocabulary and framework for future work. Adjacent fields (vision model training, multimodal training, reinforcement learning from human feedback) could adopt similar principles.
Limitations on impact: The improvements, while consistent, are modest. For practitioners already investing heavily in data curation, the marginal gains from ordering may not justify the additional complexity. The guidelines, while intuitive, may not generalize to all training regimes (e.g., multi-epoch training, continual learning).
4. Timeliness & Relevance
This is highly timely. Modern LLMs are predominantly trained for 1 epoch over massive corpora, making data ordering a first-order concern that cannot be averaged away over multiple passes. The community has heavily invested in data quality scoring (FineWeb-Edu, QuRating), creating the exact infrastructure this paper leverages. The work fills a clear gap between "what data to use" and "how to present it."
5. Strengths & Limitations
Key Strengths:
Notable Weaknesses:
Additional Observations
The connection to optimization theory could be strengthened—why do these particular orderings improve training? The gradient diversity and loss landscape arguments are empirical but lack theoretical grounding. A formal analysis connecting data ordering to convergence rates or generalization bounds would significantly elevate the contribution.
The paper's framing as "demystifying" is somewhat overstated—the guidelines are useful but largely intuitive, and the mystery of *why* they work at the optimization level remains.
Generated May 29, 2026
Comparison History (17)
Paper 2 addresses a critical bottleneck in LLM agent reliability—silent regression during self-improvement—by introducing a rigorous gating mechanism. Its focus on skill transferability across models and regression-aware learning provides a highly novel and methodologically rigorous approach. While Paper 1 offers useful guidelines for data organization during training, Paper 2's solution to dynamic agent improvement and robust empirical validation across domains suggests a broader potential impact on the rapidly growing field of autonomous AI agents.
Paper 2 has higher estimated impact due to its timely framing of a core, under-addressed deployment problem: longitudinal reliability of persistent agents. It introduces a benchmark (AgingBench) with explicit aging mechanisms and mechanism-level diagnostics, enabling reproducible evaluation and actionable repair targeting across models and memory policies—broadly relevant to LLM agents, HCI, systems, and reliability engineering. Paper 1 offers useful, low-overhead data ordering heuristics for training efficiency, but its scope is narrower and may yield incremental gains relative to the rapidly evolving training stack, whereas lifespan evaluation is likely to become a standard requirement for deployed agent systems.
Paper 2 addresses a fundamental and widely applicable challenge in LLM training—data organization—which affects the entire AI/ML community. Its practical guidelines (Boundary Sharpening, Cyclic Scheduling, etc.) and methods (STR, SAW) are immediately actionable across scales, backed by Microsoft and open-sourced. Paper 1, while technically impressive as a clean-room UB implementation with strong performance gains, targets a narrower hardware/networking audience and serves primarily as a validation of Huawei's existing specification rather than introducing a fundamentally new paradigm. The breadth of impact and timeliness of LLM training optimization gives Paper 2 the edge.
Paper 2 addresses a fundamental and broadly applicable challenge in LLM training—data organization—that impacts the entire field regardless of application domain. Its systematic formalization of guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) and novel methods (STR, SAW) validated across multiple scales offer wide utility. The open-source contribution from Microsoft further amplifies impact. Paper 1, while valuable for medical AI safety, addresses a narrower domain with incremental improvements (3-5% reduction in unsafe outputs) and a small evaluation study (30 vignettes), limiting its broader scientific influence.
Paper 2 addresses a fundamental and broadly applicable aspect of LLM training—data organization—that impacts virtually all LLM development. It provides systematic, generalizable guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) with minimal computational overhead, making it immediately practical. The work spans both pre-training and SFT stages across multiple scales, demonstrating broad applicability. Paper 1, while innovative in combining LLMs with TSFMs for time series forecasting, targets a narrower domain. Paper 2's insights are more foundational and likely to influence a wider range of future research and practice.
Paper 2 addresses a fundamental and broadly applicable challenge in LLM training—data organization—with systematic guidelines and methods (STR, SAW) validated across multiple scales and stages. Its findings are relevant to virtually all LLM practitioners, backed by Microsoft research with open-source code, and touch on the universal bottleneck of training efficiency. Paper 1, while novel in proposing LLM-native service discovery taxonomies (A2X), addresses a narrower problem in the emerging but still niche Internet of Agents ecosystem. Paper 2's breadth of impact across the entire LLM training community gives it higher potential scientific impact.
Paper 1 addresses a foundational challenge in LLM training efficiency—data organization—which has broad, immediate implications for reducing computational overhead and improving performance across the AI community. Its systematic guidelines and robust empirical validation across model scales offer highly practical and scalable solutions. While Paper 2 presents an innovative approach to multi-agent scientific workflows, its impact is more specialized compared to the universal applicability and high resource relevance of foundational LLM training optimizations.
Paper 1 introduces a novel and important security threat model ('Sleeper Attack') for LLM agents that addresses a critical gap in AI safety research. The formalization of persistent, cross-interaction adversarial attacks is highly novel and timely given the rapid deployment of LLM agents with tool use and memory. It has broad implications for AI safety, security, and responsible deployment. Paper 2, while practically useful, addresses a more incremental optimization problem (data ordering for training) with guidelines that, though systematic, represent a narrower contribution. The security implications of Paper 1 are likely to generate more follow-up research and real-world impact.
Paper 2 addresses a fundamental bottleneck in LLM development (training efficiency) with rigorous empirical experiments, novel data ordering methods, and open-source code. Its direct improvements to model performance and stability are highly actionable for researchers and practitioners. In contrast, Paper 1 offers a conceptual framework for management and governance, which, while timely, lacks the quantitative methodological rigor and immediate foundational impact on AI capabilities demonstrated by Paper 2.
Paper 1 addresses a fundamental and broadly applicable problem in LLM training—data organization—that affects virtually all large-scale model development. It provides systematic, generalizable guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) validated across multiple scales and stages, with minimal computational overhead. Its findings can be widely adopted across the entire LLM community. Paper 2, while innovative in embodied AI with its contrastive memory internalization approach, targets a narrower domain (Minecraft agents) with more limited immediate applicability and community reach.
Paper 2 addresses a fundamental challenge in AI: optimizing LLM training efficiency. Its guidelines and methods for data organization can broadly impact the vast community developing foundation models, reducing computational costs and improving performance. In contrast, while Paper 1 presents a novel application of multimodal models to traffic signal control, its scope is largely limited to transportation engineering and smart city infrastructure, resulting in a narrower potential scientific impact.
Paper 1 introduces a highly novel approach (latent reasoning) to address a critical bottleneck in real-world LLM deployment: the high latency and token cost of reasoning-based safety guardrails. By achieving a 12.9X speedup and 22.4X reduction in token usage without sacrificing safety performance, it offers massive practical utility. While Paper 2 provides valuable insights into data organization for LLM training, curriculum learning and data ordering are more saturated fields, making the latent space reasoning paradigm in Paper 1 a more significant structural innovation with broader immediate deployment impact.
Paper 2 likely has higher scientific impact: it proposes broadly applicable, low-overhead guidelines and concrete methods for data ordering that can improve stability/efficiency across pre-training and SFT, affecting many LLM pipelines and reducing compute/data costs. This is timely and widely relevant to both academia and industry, with clear real-world adoption potential and reproducibility संकेत (code link, multi-scale experiments). Paper 1 is novel and large-scale, but its impact is narrower (value simulation/alignment) and more sensitive to prompt-based methodology and construct validity when mapping human value theory to LLM behavior.
Paper 2 addresses a fundamental challenge in the foundational training of Large Language Models (data organization and efficiency), which has widespread implications across the entire AI field. In contrast, Paper 1 focuses on a niche application (child-AI co-creation in board games). Paper 2's extensive experiments across scales and training stages, combined with its broad applicability to any LLM development, give it a significantly higher potential for scientific and practical impact.
Paper 1 addresses a fundamental and broadly applicable problem in LLM training—data organization—with systematic guidelines and methods validated across multiple scales and settings. Its findings (STR, SAW ordering methods) can improve training efficiency for the entire LLM community, affecting pre-training and SFT broadly. Paper 2, while creative and impressive in combining LLMs with rule-based poker skills, addresses a narrower domain (poker/game playing) with more limited generalizability. The methodological rigor, breadth of impact, and practical utility of Paper 1's contributions to the foundational LLM training pipeline give it higher potential scientific impact.
Paper 2 addresses a fundamental challenge in the development of Large Language Models—data organization during training. Improving training efficiency and performance across both pre-training and supervised fine-tuning stages has a profound and immediate impact on the broader AI community, affecting how foundation models are built. While Paper 1 offers a valuable training-free method for multi-agent systems, the guidelines and methods in Paper 2 have wider applicability, greater foundational relevance, and the potential to influence a larger spectrum of LLM research and deployment.
Paper 1 addresses a fundamental and broadly applicable problem in LLM training—data organization—that impacts the entire LLM community. Its systematic framework with formalized guidelines (Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, Local Diversity) and validated methods (STR, SAW) across multiple scales offers practical, generalizable contributions. Paper 2, while methodologically rigorous and addressing important evaluation gaps in LLM trading agents, targets a narrower domain (financial trading benchmarks). Paper 1's breadth of impact across all LLM training scenarios gives it higher potential scientific impact.