9 papers
CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training
Fangfei Li, Chenyang Zhao, Long Wang +3
Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents CLAP (Closed-Loop Agent Pos…
LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination
Ziming Zhu, Chenglong Wang, Haosong Xv +8
Despite the remarkable progress of modern machine translation (MT) systems on general-domain texts, translating structured LaTeX-formatted documents remains a significant challenge…
Training LLMs with Fault Tolerant HSDP on 100,000 GPUs
Omkar Salpekar, Rohan Varma, Kenny Yu +20
Large-scale training systems typically use synchronous training, requiring all GPUs to be healthy simultaneously. In our experience training on O(100K) GPUs, synchronous training r…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs
Shihao Qi, Jie Ma, Ziang Yin +5
Existing methods usually leverage a fixed strategy, such as natural language reasoning, code-augmented reasoning, tool-integrated reasoning, or ensemble-based reasoning, to guide L…
From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluation
Seokhee Hong, Sunkyoung Kim, Guijin Son +3
The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applica…