106 citations · 171 across the 16 of their papers we have counts for
19 papers
Budgeted KV-Cache Repair for Evolving Contexts
Han Yu, Wenhui Zhu, Jincheng Cao +15
Language-model agents revise their working context as information becomes obsolete, but deleting text does not erase its influence on cached computation. Later key-value (KV) state…
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu +2
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recen…
Smaller Models, Better Rejects: Preference Distillation Scaling
Rui Cai, Wenhui Zhu, Xiwen Chen +15
Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated failures are the most informat…
LatentPress: Context Compression Beyond Text and Vision
Zhengze Zhou, Yuanda Xu, Hejian Sang +3
Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. We introduce LatentPress, which…
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
Yuanda Xu, Zhengze Zhou, Hejian Sang +6
Agentic reinforcement learning requires assigning credit to environment-facing actions such as searches, clicks, edits, navigation commands, and object interactions. Standard GRPO…
TREK: Distill to Explore, Reinforce to Refine
Yuanda Xu, Zhengze Zhou, Kayhan Behdin +10
Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard prompts whose correct solution m…
TIP: Token Importance in On-Policy Distillation
Yuanda Xu, Hejian Sang, Zhengze Zhou +3
On-policy knowledge distillation (OPD) trains a student on its own rollouts under token-level supervision from a teacher. Not all token positions matter equally, but existing views…
Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
Yuanda Xu, Hejian Sang, Zhengze Zhou +3
In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated to the model and reward density where it is most informative.…
Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
Jinxiao Zhang, Runmin Dong, Xiyong Wu +14
Earth observation is becoming one of the largest data-producing activities in science, yet current pipelines still treat compression as a storage and transmission tool rather than…
PACED: Distillation and On-Policy Self-Distillation at the Frontier of Student Competence
Yuanda Xu, Hejian Sang, Zhengze Zhou +2
Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve. We empirically show that this in…
CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
Hejian Sang, Yuanda Xu, Zhengze Zhou +3
Reasoning models often generate far more tokens than a task requires, which raises inference cost and can compound errors. We introduce CRISP (Compressed Reasoning via Iterative Se…
Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning
Yuanda Xu, Hejian Sang, Zhengze Zhou +2
Reinforcement Learning with Verifiable Rewards (RLVR) has become the leading paradigm for enhancing reasoning in Large Language Models (LLMs). However, standard RLVR algorithms suf…
Not all tokens are needed(NAT): token efficient reinforcement learning
Hejian Sang, Yuanda Xu, Zhengze Zhou +2
Reinforcement learning (RL) has become a key driver of progress in large language models, but scaling RL to long chain-of-thought (CoT) trajectories is increasingly constrained by…
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
Xiaojian Zhang, Xiang Yan, Zhengze Zhou +2
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the non…
Model-free Subsampling Method Based on Uniform Designs
Mei Zhang, Yongdao Zhou, Zheng Zhou +1
Subsampling or subdata selection is a useful approach in large-scale statistical learning. Most existing studies focus on model-based subsampling methods which significantly depend…
S-LIME: Stabilized-LIME for Model Explanation
Zhengze Zhou, Giles Hooker, Fei Wang
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are…
Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
Xilei Zhao, Zhengze Zhou, Xiang Yan +1
Machine learning has proved to be very successful for making predictions in travel behavior modeling. However, most machine-learning models have complex model structures and offer…
Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhengze Zhou, Giles Hooker
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biase…
Approximation Trees: Statistical Stability in Model Distillation
Yichen Zhou, Zhengze Zhou, Giles Hooker
This paper examines the stability of learned explanations for black-box predictions via model distillation with decision trees. One approach to intelligibility in machine learning…