新的数据策略提升大语言模型强化学习性能

报道来源 [5]

1. arXiv cs.CL TIER_1 English(EN) · Chenhao Dang, Jing Ma, Mingjie Liao · 2026-06-24 04:00

   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 · 2026-06-23 04:32

   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) · 2026-06-23 00:00

   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 · 2026-06-21 02:19

   以合适的节奏学习：自适应数据调度改进LLM强化学习

   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) · 2026-06-17 00:00

   超越奖励工程：长上下文强化学习的数据配方

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