data-refinement

MMSearch-OPD 提出了一种结合可控数据精炼与联合强化学习和在线策略蒸馏的方法，以提升长时程多模态搜索智能体的能力，通过将简单问题扩展为困难且可学习的训练样本取得显著改进。

UltraX proposes a function-calling refinement framework for large-scale pre-training data that introduces insertion alongside deletion and modification, enabling fine-grained instance-level editing. It builds a reliable program-supervision generation pipeline and demonstrates improved data efficiency and model performance when pretraining 1B models from scratch.

Introduces CDR-Bench, a benchmark with 3,462 tasks to evaluate LLMs' ability to faithfully execute compositional, order-sensitive data refinement recipes. Experiments on 10+ LLMs reveal significant performance degradation in compositional and order-sensitive settings, highlighting a lack of procedural faithfulness.

RAFT is a two-stage framework for domain-specific fine-tuning of LLMs that addresses catastrophic forgetting by refining supervision data and using on-policy distillation with adaptive loss balancing, achieving significant improvements on domain accuracy while recovering general capabilities.