1 citations · 1 across the 11 of their papers we have counts for
13 papers
TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models
Yidong Wang, Yan Zhan, Ziteng Feng +16
Reward models are a bottleneck for reinforcement learning in embodied AI. Long-horizon robotic manipulation requires scalable vision feedback beyond handcrafted rewards or task-spe…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies o…
Meta-Aligner: Bidirectional Preference-Policy Optimization for Multi-Objective LLMs Alignment
Wenzhe Xu, Biao Liu, Yiyang Sun +2
Multi-Objective Alignment aims to align Large Language Models (LLMs) with diverse and often conflicting human values by optimizing multiple objectives simultaneously. Existing meth…
Seed2Scale: A Self-Evolving Data Engine with Parallel Worlds Expansion for Scalable Robot Learning
Cong Tai, Zhaoyu Zheng, Haixu Long +14
Existing data generation methods for robot learning suffer from limited exploration, embodiment gaps, low signal-to-noise ratios, and domain shifts, leading to performance degradat…
Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai +8
Due to constraints on privacy, cost, and latency, on-premise deployment of small models is increasingly common. However, most practical pipelines stop at supervised fine-tuning (SF…
Preference Orchestrator: Prompt-Aware Multi-Objective Alignment for Large Language Models
Biao Liu, Ning Xu, Junming Yang +1
While Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, aligning these models with varying human preferences…
Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai +8
Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised seriou…
Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization
Junming Yang, Ning Xu, Biao Liu +2
Preference optimization is crucial for aligning large language models (LLMs) with human values and intentions. A significant challenge in this process is the distribution mismatch…
Negative-Prompt-driven Alignment for Generative Language Model
Shiqi Qiao, Ning Xv, Biao Liu +1
Large language models have achieved remarkable capabilities, but aligning their outputs with human values and preferences remains a significant challenge. Existing alignment method…
Progressively Label Enhancement for Large Language Model Alignment
Biao Liu, Ning Xu, Xin Geng
Large Language Models (LLM) alignment aims to prevent models from producing content that misaligns with human expectations, which can lead to ethical and legal concerns. In the las…
Can Class-Priors Help Single-Positive Multi-Label Learning?
Biao Liu, Ning Xu, Jie Wang +1
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Exi…
On the Robustness of Average Losses for Partial-Label Learning
Jiaqi Lv, Biao Liu, Lei Feng +6
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (I…