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cs.LG2026
miniTE: Diagnosing and Bridging the Execution Gap in Native NVFP4 Reinforcement Learning
Zhen Li, Mingfa Feng, Yiming Zhang +6
Native NVFP4 promises to accelerate reinforcement learning (RL) for large language models (LLMs), but achieving stable learning across separate training and sampling engines remain…
cs.LG2025
InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models
Yanggan Gu, Yuanyi Wang, Zhaoyi Yan +4
Model fusion combines multiple Large Language Models (LLMs) with different strengths into a more powerful, integrated model through lightweight training methods. Existing works on…