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arxiv: 2606.24133 · v1 · pith:KYFTQOSRnew · submitted 2026-06-23 · 💻 cs.LG · cs.CL

Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

Chenhao Dang , Jing Ma , Mingjie Liao This is my paper

Pith reviewed 2026-06-26 00:44 UTC · model grok-4.3

classification 💻 cs.LG cs.CL
keywords LLM pre-trainingonline data mixingreinforcement learningdata schedulermulti-objective rewardSoft Actor-CriticThe Piletraining efficiency
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The pith

Holistic Data Scheduler reaches target perplexity on The Pile with 44% fewer training iterations using multi-objective RL.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents the Holistic Data Scheduler as a way to adaptively mix training data for LLMs during pre-training by treating the mixing ratios as actions in a reinforcement learning setup. It employs the Soft Actor-Critic algorithm guided by a reward that combines data quality signals, how domains affect each other's loss, and changes in model weight norms. This multi-perspective approach is shown to speed up reaching good validation performance and improve results on downstream tasks like MMLU compared to previous single-objective mixing methods. A sympathetic reader would care because better data scheduling directly reduces the compute needed to train capable models.

Core claim

HDS formulates the data scheduling challenge as a reinforcement learning problem in a continuous control space and leverages the Soft Actor-Critic (SAC) algorithm. At the core of HDS lies a novel multi-objective, holistic reward function that integrates three critical perspectives: a data-driven reward for quality, a loss-driven reward capturing inter-domain influence, and a model-driven reward based on weight norms. On The Pile benchmark, HDS reaches the final validation perplexity of the next best method with 44% fewer training iterations and achieves a 7.2% improvement on the MMLU 0-shot task.

What carries the argument

The multi-objective holistic reward function that combines data-driven quality, loss-driven inter-domain influence, and model-driven weight norms to guide the SAC policy in adjusting data mixtures.

If this is right

  • Reaches the same final validation perplexity with 44% fewer training iterations on The Pile.
  • Achieves a 7.2% improvement on the MMLU 0-shot task.
  • Shows consistent gains on other benchmarks.
  • Enhances both training efficiency and final model capability across various model sizes.
  • Provides a stable training signal without extensive per-benchmark retuning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Applying similar multi-objective rewards could improve data efficiency in other machine learning domains such as computer vision training.
  • The method might allow for even larger efficiency gains if the reward components are dynamically weighted during training.
  • Testing HDS on models beyond the sizes experimented with could reveal scaling behaviors of the scheduling benefits.
  • Integrating this scheduler with other optimization techniques like curriculum learning could yield additive improvements.

Load-bearing premise

The three-component reward function supplies a sufficiently stable and non-overfitting training signal for the SAC policy across different model sizes and data distributions without extensive per-benchmark retuning.

What would settle it

Running HDS on a new large model or different data distribution where it fails to match the 44% iteration reduction or requires heavy reward weight retuning would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.24133 by Chenhao Dang, Jing Ma, Mingjie Liao.

Figure 1
Figure 1. Figure 1: Performance evaluation of the Holistic Data Scheduler (HDS). All results are from a Pythia-1B model trained for 50 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of the Holistic Data Scheduler (HDS) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The network architecture for the actor and critic. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Test perplexity on the average and across the 22 individual domains of The Pile. The horizontal axis indicates the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study of the reward components. The vali [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Evolution of domain weights for five selected do [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Evolution of the L2 norm for selected layer weights during the training process. Each subplot corresponds to a different [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original abstract

The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training. Online Data Mixing (ODM), the technique of adaptively adjusting data mixtures during training, has emerged as a promising direction to improve efficiency. However, existing methods are constrained by their reliance on a singular optimization perspective, which fundamentally overlooks the need for complex LLM pre-training to consider the dynamic data composition from multiple dimensions. To overcome this limitation, we introduce the Holistic Data Scheduler (HDS), a novel online data mixing framework. HDS formulates the data scheduling challenge as a reinforcement learning problem in a continuous control space and leverages the Soft Actor-Critic (SAC) algorithm for its stability and sample efficiency in exploring the high-dimensional policy space. At the core of HDS lies a novel multi-objective, holistic reward function that integrates three critical perspectives: a data-driven reward for quality, a loss-driven reward capturing inter-domain influence, and a model-driven reward based on weight norms. To validate our design and determine its optimal configuration, we conducted systematic experiments on LLMs of various sizes. On The Pile benchmark, HDS reaches the final validation perplexity of the next best method with 44% fewer training iterations. Furthermore, it achieves a 7.2% improvement on the MMLU 0-shot task along with consistent gains on other benchmarks, showcasing its ability to enhance both training efficiency and final model capability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Holistic Data Scheduler (HDS), an online data mixing framework for LLM pre-training that casts data scheduling as a continuous-control RL problem solved via Soft Actor-Critic (SAC). Its central contribution is a three-component multi-objective reward (data-driven quality, loss-driven inter-domain influence, model-driven weight norms) intended to capture multiple perspectives simultaneously. Systematic experiments on LLMs of varying sizes are reported to yield a 44% reduction in training iterations to reach the next-best final validation perplexity on The Pile, together with a 7.2% absolute gain on MMLU 0-shot and consistent improvements on other benchmarks.

Significance. If the reported efficiency and capability gains prove robust, the work would offer a practical route to more sample-efficient pre-training by replacing single-objective heuristics with a learned, multi-perspective scheduler. The use of SAC in a high-dimensional continuous mixing space and the explicit attempt to integrate data, loss, and model signals are technically interesting directions for the data-mixing literature.

major comments (3)
  1. [Abstract] Abstract and experimental sections: the headline claims (44% fewer iterations on The Pile, 7.2% MMLU gain) are presented without any description of the baselines, number of runs, statistical tests, or implementation equations for the three reward components. Without these details it is impossible to determine whether the performance differences are load-bearing or sensitive to post-hoc choices.
  2. [Reward function] Reward-function description (presumably §3 or §4): the manuscript states that systematic experiments were run on LLMs of various sizes, yet supplies no evidence that the relative weighting of the three reward terms was held fixed across model scales and data distributions. If per-size or per-benchmark re-balancing is required to prevent instability or collapse of the SAC policy, the reported gains would not transfer and the “holistic” framing would be undercut.
  3. [Experiments] Experimental protocol: the abstract asserts “systematic experiments” but the provided text contains no tables, figures, or appendices showing learning curves, ablation of individual reward components, or sensitivity to the SAC hyperparameters. These omissions make the central efficiency claim unverifiable from the manuscript as presented.
minor comments (2)
  1. Notation for the three reward terms is introduced without explicit equations or normalization details, making it difficult to reproduce the exact reward signal.
  2. [Abstract] The abstract refers to “the next best method” without naming the comparator or citing its source; this should be clarified in the main text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater clarity and verifiability in our claims. We address each major comment below and will revise the manuscript to incorporate the requested details, equations, and experimental evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract and experimental sections: the headline claims (44% fewer iterations on The Pile, 7.2% MMLU gain) are presented without any description of the baselines, number of runs, statistical tests, or implementation equations for the three reward components. Without these details it is impossible to determine whether the performance differences are load-bearing or sensitive to post-hoc choices.

    Authors: We agree that the abstract and experimental sections require additional context for full verifiability. In the revised manuscript we will expand the abstract to name the primary baselines (uniform mixing, DoReMi, and other ODM methods), state that all results are averaged over three independent runs with standard deviations, and reference the statistical tests performed. The explicit equations for the three reward components (data quality, inter-domain loss influence, and weight-norm regularization) will be moved to the main text in Section 3.2 with a pointer from the abstract. revision: yes

  2. Referee: [Reward function] Reward-function description (presumably §3 or §4): the manuscript states that systematic experiments were run on LLMs of various sizes, yet supplies no evidence that the relative weighting of the three reward terms was held fixed across model scales and data distributions. If per-size or per-benchmark re-balancing is required to prevent instability or collapse of the SAC policy, the reported gains would not transfer and the “holistic” framing would be undercut.

    Authors: The relative weights of the three reward terms were held fixed across all model scales and data distributions in our experiments; this is stated in the hyperparameter appendix of the original submission. To make this explicit and address the concern, the revision will add a dedicated paragraph in Section 4 together with a sensitivity table confirming that the same fixed weights were used without per-scale re-balancing and that policy collapse was not observed. revision: yes

  3. Referee: [Experiments] Experimental protocol: the abstract asserts “systematic experiments” but the provided text contains no tables, figures, or appendices showing learning curves, ablation of individual reward components, or sensitivity to the SAC hyperparameters. These omissions make the central efficiency claim unverifiable from the manuscript as presented.

    Authors: We acknowledge that the current manuscript version omitted several supporting visualizations due to space limits. The revised submission will include new figures and an expanded appendix containing (i) training curves for all methods, (ii) ablations isolating each reward component, and (iii) sensitivity plots for key SAC hyperparameters (learning rate, entropy coefficient, replay buffer size). These additions will render the experimental protocol fully verifiable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The abstract formulates data scheduling as an RL problem solved by SAC and defines a three-component reward (data-driven quality, loss-driven inter-domain influence, model-driven weight norms) as independent perspectives. No equations, fitting procedures, or self-citations are shown that would reduce any claimed prediction or result to its own inputs by construction. Experimental claims rest on validation runs rather than analytic reductions, satisfying the criteria for a self-contained derivation with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract alone supplies no equations, implementation details, or explicit assumptions; therefore no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5797 in / 1181 out tokens · 27087 ms · 2026-06-26T00:44:04.155505+00:00 · methodology

discussion (0)

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Reference graph

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