1 citations · 2 across the 35 of their papers we have counts for
13 papers · 1 filter
Scaling Agentic Data for Long-Horizon Terminal Intelligence
Shun Zou, Ziyu Ma, Yi Zou +6
Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not full…
SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents
Shidong Yang, Ziyu Ma, Tongwen Huang +5
Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks. However, most RL-trained agents remain episodic and cannot accumul…
BlockPilot: Instance-Adaptive Policy Learning for Diffusion-based Speculative Decoding
Hao Zhang, Yiming Hu, Yong Wang +3
Speculative decoding accelerates inference by using a lightweight draft model to generate candidate tokens in parallel, and are then verified by the target model, enabling lossless…
Learning Agentic Policy from Action Guidance
Yuxiang Ji, Zengbin Wang, Yong Wang +6
Agentic reinforcement learning (RL) for Large Language Models (LLMs) critically depends on the exploration capability of the base policy, as training signals emerge only within its…
Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Zeguan Xiao, Xuanzhe Xu, Yun Chen +5
Large language model (LLM) unlearning aims to remove specific data influences from pre-trained model without costly retraining, addressing privacy, copyright, and safety concerns.…
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution
Shidong Yang, Ziyu Ma, Tongwen Huang +3
Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and leads to poor coverage of comp…
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Zeguan Xiao, Siqing Li, Yong Wang +4
Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM unlearning as an asymmetric…
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models
Shun Zou, Yong Wang, Zehui Chen +4
Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive (Semi-AR) decoding is widel…
FASA: Frequency-aware Sparse Attention
Yifei Wang, Yueqi Wang, Zhenrui Yue +6
The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address th…
AR-MAP: Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models?
Liang Lin, Feng Xiong, Zengbin Wang +5
Diffusion Large Language Models (DLLMs) have emerged as a powerful alternative to autoregressive models, enabling parallel token generation across multiple positions. However, pref…
Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation
Yifei Wang, Feng Xiong, Yong Wang +3
Positional bias (PB), manifesting as non-uniform sensitivity across different contextual locations, significantly impairs long-context comprehension and processing capabilities. Pr…
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation
Chen Liang, Zhifan Feng, Zihe Liu +4
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and…
Direct Alignment of Language Models via Quality-Aware Self-Refinement
Runsheng Yu, Yong Wang, Xiaoqi Jiao +2
Reinforcement Learning from Human Feedback (RLHF) has been commonly used to align the behaviors of Large Language Models (LLMs) with human preferences. Recently, a popular alternat…