62 citations · 64 across the 7 of their papers we have counts for
8 papers
TerraceMoE: A Cost Model for Hierarchical MoE All-to-All Communication
Weicheng Xue, Bingqiang Wang, Li Yuan +2
Hierarchical two-hop dispatch can reduce slow-fabric traffic in expert-parallel Mixture-of-Experts training, but it adds a second collective and an arrival-side operator chain. We…
Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals
Shuo Yang, Jinda Lu, Chiyu Ma +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a central paradigm for scaling LLM reasoning, yet its optimization often suffers from training instability and…
One-Way Policy Optimization for Self-Evolving LLMs
Shuo Yang, Jinda Lu, Kexin Huang +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs). However, the sparsity of b…
GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
Kun-Peng Ning, Jia-Yu Yao, Yu-Yang Liu +2
Large Language Models (LLMs), such as GPT, are considered to learn the latent distributions within large-scale web-crawl datasets and accomplish natural language processing (NLP) t…
Sparse Orthogonal Parameters Tuning for Continual Learning
Kun-Peng Ning, Hai-Jian Ke, Yu-Yang Liu +3
Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. These method…
Is Parameter Collision Hindering Continual Learning in LLMs?
Shuo Yang, Kun-Peng Ning, Yu-Yang Liu +4
Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deploym…
PiCO: Peer Review in LLMs based on the Consistency Optimization
Kun-Peng Ning, Shuo Yang, Yu-Yang Liu +5
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations…
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu +3
Large Language Models (LLMs), including GPT-3.5, LLaMA, and PaLM, seem to be knowledgeable and able to adapt to many tasks. However, we still cannot completely trust their answers,…