2 citations · 3 across the 17 of their papers we have counts for
5 papers · 1 filter
Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning
Junlin Wu, Xianrui Zhong, Jiashuo Sun +4
Large language models (LLMs) have demonstrated remarkable advances in reasoning capabilities. However, their performance remains constrained by limited access to explicit and struc…
DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning
Yifan Wang, Bolian Li, Junlin Wu +5
Real-world large language model deployments (e.g., conversational AI systems, code generation assistants) naturally generate abundant implicit user dissatisfaction (DSAT) signals,…
From Personal to Collective: On the Role of Local and Global Memory in LLM Personalization
Zehong Wang, Junlin Wu, ZHaoxuan Tan +4
Large language model (LLM) personalization aims to tailor model behavior to individual users based on their historical interactions. However, its effectiveness is often hindered by…
Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner
Bolian Li, Yanran Wu, Xinyu Luo +1
Aligning large language models (LLMs) with human preferences has become a critical step in their development. Recent research has increasingly focused on test-time alignment, where…
Cascade Reward Sampling for Efficient Decoding-Time Alignment
Bolian Li, Yifan Wang, Anamika Lochab +2
Aligning large language models (LLMs) with human preferences is essential for their applications. Recently, decoding-time alignment has emerged as an effective plug-and-play techni…