1 citations · 1 across the 8 of their papers we have counts for
10 papers · 1 filter
SEER: Long-Context Reasoning via Selective Visual-Text Compression
Jiawei Xu, Zhilin Zhai, Jinrui Fang +6
Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a p…
When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning
Xiuyi Lou, Zicheng Xu, Yu-Neng Chuang +4
Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely used critic-free RL methods rel…
DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings
Wenya Xie, Shengming Zhou, Zelin Li +9
LLM agents increasingly act as personal assistants that must remember a user's profile over months: who they are (attributes), what they routinely do (habits), and what they prefer…
Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Zicheng Xu, Ruixuan Zhang, Yu-Neng Chuang +7
Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on unif…
Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
Haiyan Zhao, Zirui He, Guanchu Wang +3
Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own ac…
Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +4
On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify a failure mode of OPD: as t…