UltraX: LLM Training Data Refinement Framework
zibuyu9 · x · 2026-07-15
OpenBMB has proposed UltraX, a new large language model training data refinement framework. Instead of rewriting raw data entirely, it uses structured editing functions for granular insertions, deletions, and modifications to enhance data quality and efficiency.
Key highlights:
- Supports function-calling style refinement for fine-grained edits like insertion, deletion, and modification.
- Generates more reliable supervision signals via the LAM + DCR pipeline for scalable data rewriting.
- In 1B parameter model pre-training experiments, the authors report achieving the best average performance across 5 corpora.
- Accompanied by resources on paper, GitHub, Hugging Face, and ModelScope.
More from Research
- HarmonicMath says Lean autonomously solved eight previously studied open problems — MarioKrenn6240 · 2026-07-21
- SeeSE3 finds 3D structure emerging in frozen vision features and camera-pose alignment — ducha_aiki · 2026-07-21
- Open-source MCP server connects Screener.in to live financial data for LLM research workflows — ashutosh_811 · 2026-07-21
- A parody prompt asks for a planetary crystal factory inventory and parity report — Promptmethus · 2026-07-21
- PRA hits new image-generation SOTA with 511M parameters and FID 1.94 — jiqizhixin · 2026-07-21
- SUFLECA shows NOC-based correspondence can improve CAD-to-image alignment — ducha_aiki · 2026-07-21