1 citations · 1 across the 9 of their papers we have counts for
10 papers
WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training
Zehao Chen, Gongxun Li, Tianxiang Ai +9
On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch of offline distillation. The sa…
Multi-Objective Exploration and Preference Optimization via Mutual Information
Hongyan Xie, Yikun Ban, Ruiyu Fang +4
Aligning large language models with diverse and heterogeneous human values requires multi-objective alignment methods to effectively trade off conflicting preference dimensions. Cu…
Policy Improvement Reinforcement Learning
Huaiyang Wang, Xiaojie Li, Xiaohan Wang +10
Reinforcement learning has become a central post-training paradigm for improving LLM and agent capabilities. Yet existing RL post-training methods share a common blind spot: they c…
UniARM: Towards a Unified Autoregressive Reward Model for Multi-Objective Test-Time Alignment
Hongyan Xie, Yikun Ban, Ruiyu Fang +6
Multi-objective alignment aims to align LLM responses with multiple human preference objectives. Among existing methods, guiding the generation of frozen LLMs through autoregressiv…
Does Your Reasoning Model Implicitly Know When to Stop Thinking?
Zixuan Huang, Xin Xia, Yuxi Ren +11
Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approa…
Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
Zehao Chen, Gongxun Li, Tianxiang Ai +9
As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training…
Your Group-Relative Advantage Is Biased
Fengkai Yang, Zherui Chen, Xiaohan Wang +10
Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such…
Real-Time Aligned Reward Model beyond Semantics
Zixuan Huang, Xin Xia, Yuxi Ren +10
Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is susceptible to reward overoptim…
LLMBoost: Make Large Language Models Stronger with Boosting
Zehao Chen, Tianxiang Ai, Yifei Li +11
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or fina…
Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
Zixuan Huang, Yikun Ban, Lean Fu +4
Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its performance is highly depen…