2 citations · 2 across the 7 of their papers we have counts for
11 papers
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…
CoCoSI: Collaborative Cognitive Map Construction for Spatial Intelligence
Yiming Zhang, Ruoxuan Cao, Zhihang Zhong
Spatial intelligence is a key frontier for multimodal large language models (MLLMs), enabling them to reason about the physical world from visual experience. Inspired by human spat…
SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents
Wentao Hu, Zhendong Chu, Yiming Zhang +6
Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches…
ProvMind: Provenance-grounded reasoning for materials synthesis
Yiming Zhang, Ryo Tamura, Koji Tsuda
Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text…
Model Merging Scaling Laws in Large Language Models
Yuanyi Wang, Yanggan Gu, Yiming Zhang +6
We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative rule that predicts returns as we…
A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models
Wenjun Wang, Shuo Cai, Congkai Xie +7
The immense computational cost of training Large Language Models (LLMs) presents a major barrier to innovation. While FP8 training offers a promising solution with significant theo…
InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion
Yuanyi Wang, Zhaoyi Yan, Yiming Zhang +4
Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherits their complementary strengths.…
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…
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Congkai Xie, Shuo Cai, Wenjun Wang +17
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as…
InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion
Zhaoyi Yan, Yiming Zhang, Baoyi He +7
We introduce InfiFusion, an efficient training pipeline designed to integrate multiple domain-specialized Large Language Models (LLMs) into a single pivot model, effectively harnes…
Unconstrained Model Merging for Enhanced LLM Reasoning
Yiming Zhang, Baoyi He, Shengyu Zhang +12
Recent advancements in building domain-specific large language models (LLMs) have shown remarkable success, especially in tasks requiring reasoning abilities like logical inference…