2 citations · 4 across the 9 of their papers we have counts for
11 papers
Divergence Decoding: Training-Free Capability Fusion
Yimi Wang, Hao Li, Shuo Yang +6
While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(specialists), while knowledgea…
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…
Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era
Qiuhe Hong, Yuyang Liu, Shuo Yang +3
Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging paradigm that couples Multimod…
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step
Zheyuan Liu, Munan Ning, Qihui Zhang +8
Current text-to-image (T2I) generation models struggle to align spatial composition with the input text, especially in complex scenes. Even layout-based approaches yield suboptimal…
Look-Back: Implicit Visual Re-focusing in MLLM Reasoning
Shuo Yang, Yuwei Niu, Yuyang Liu +3
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in multimodal reasoning. However, they often excessively rely on textual information during the later sta…
AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin
Shuo Yang, Qihui Zhang, Yuyang Liu +7
Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We…
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
Qihui Zhang, Munan Ning, Zheyuan Liu +7
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations…
Next Patch Prediction for Autoregressive Visual Generation
Yatian Pang, Peng Jin, Shuo Yang +8
Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision task…
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…