Author

Hao Yu

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Jul 2026

Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning

Search-on-Graph-R1 (\sogrone{}), which internalizes this navigation into a compact 8B model through supervised fine-tuning (SFT) followed by reinforcement learning (RL), which surpasses every frozen frontier-LLM system in their comparison and posts the strongest results on CWQ of any system the authors compare against.

J. Sun, Hao Yu, Fengran Mo et al. · arXiv.org · 1 citation · ⚡1

Jul 2026

QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides

This work identifies activation error, rather than weight error, as the dominant source of FP4 RL instability: weights can be synchronized and aligned by a shared quantization-dequantization path, whereas activations are recomputed online and error is amplified by the coarse E2M1 grid.

Zhengyang Zhuge, Hao Yu, Xin Wang et al. · arXiv.org · 0 citations

#natural language process... Preprint Aug 2026

H-Scale: Hessian-Guided Scale Refinement for NVFP4 Sub-Byte LLM Inference

H-Scale is a lightweight post-processing method for NVFP4 per-group scale refinement that selects hardware-valid group scales using a diagonal second-order proxy derived from calibration activations, thereby targeting layer output perturbation more directly.

Hao Yu, Zheng Li, Dayiheng Liu et al. · 0 citations