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New LLM Training Methods Optimize Data Scheduling for Efficiency and Performance

By PulseAugur Editorial · [5 sources] ·

Researchers have developed new methods for optimizing the training of large language models (LLMs) through advanced data scheduling techniques. One approach, the Holistic Data Scheduler (HDS), uses multi-objective reinforcement learning to dynamically adjust data mixtures during pre-training, leading to significant improvements in training efficiency and model performance on benchmarks like The Pile and MMLU. Another method, Adaptive Data Scheduling (ADS), focuses on improving reinforcement learning post-training by moving beyond uniform data sampling to an adaptive distribution over semantic clusters and policy-boundary samples, showing gains in reasoning benchmarks. Additionally, a data-centric approach using curated datasets and a minimal GRPO setup has demonstrated substantial improvements in long-context reasoning for LLMs, outperforming prior reinforcement learning methods. AI

IMPACT These advancements in data scheduling and reinforcement learning techniques promise to accelerate LLM training and enhance their reasoning capabilities, particularly for long-context tasks.

RANK_REASON Multiple research papers introducing novel methods for LLM training and reinforcement learning.

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AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New LLM Training Methods Optimize Data Scheduling for Efficiency and Performance

COVERAGE [5]

  1. arXiv cs.CL TIER_1 English(EN) · Chenhao Dang, Jing Ma, Mingjie Liao ·

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

    arXiv:2606.24133v1 Announce Type: cross 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 mi…

  2. arXiv cs.CL TIER_1 English(EN) · Mingjie Liao ·

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

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    A novel online data mixing framework called Holistic Data Scheduler uses reinforcement learning with a multi-objective reward function to optimize large language model pre-training efficiency and performance.

  4. arXiv cs.CL TIER_1 English(EN) · Vladimir Braverman ·

    Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning

    Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on uniform data sampling, which ignores the semantic structure of the training data and the changing capabi…

  5. Hugging Face Daily Papers TIER_1 English(EN) ·

    Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning

    Data-centric approach using curated datasets and minimal GRPO setup significantly improves long-context reasoning in large language models, outperforming prior reinforcement learning methods.

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