10 citations · 11 across the 8 of their papers we have counts for
10 papers
TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery
Yuanzhe Li, Pengxin Wang, Yuxin Ren +5
Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic tasks suggest potential for a…
Learning Perturbation Robust Policies for LLM Agents with Stable Optimization
Pengxin Wang, Yuanzhe LI, Yuxin Ren +2
Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policies can be s…
From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation
Yuxin Ren, Maxwell D Collins, Miao Hu +1
Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensive. Replacing attention with si…
Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao +5
Reranking, as the final stage of recommender systems, plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking h…
On Affine Homotopy between Language Encoders
Robin SM Chan, Reda Boumasmoud, Anej Svete +8
Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis:…
Non-autoregressive Generative Models for Reranking Recommendation
Yuxin Ren, Qiya Yang, Yichun Wu +3
Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage re…
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Yuxin Ren, Qipeng Guo, Zhijing Jin +4
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a…
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
Yuxin Ren, Zihan Zhong, Xingjian Shi +3
It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher t…
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
Manyi Zhang, Yuxin Ren, Zihao Wang +1
Instance-dependent label noise is realistic but rather challenging, where the label-corruption process depends on instances directly. It causes a severe distribution shift between…
Exploring Extreme Parameter Compression for Pre-trained Language Models
Yuxin Ren, Benyou Wang, Lifeng Shang +2
Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises ma…