1 citations · 1 across the 7 of their papers we have counts for
11 papers
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Kuan Zhang, Yukun Chen, Zhihao Yang +8
Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these…
Source-preserving alignment for robust evidence localization in scientific PDFS
Zihao Liu, Wei Yang, Zixiao Dong +4
Scientific information-extraction systems often return a claim with an evidence string, which users must locate in the original PDF. This is challenging because the extracted evide…
Adapt Semantics, Not Structure: Few-Instance Schema Calibration for Scientific PDF Extraction
Zixiao Dong, Wei Yang, Zihao Liu +4
A well-designed extraction schema is not necessarily ready for reliable LLM execution. When only limited verified extractions are available, manually tuning hundreds of field defin…
From Weak Task Specifications to Scientific Extraction Agents: Optimizing Task Construction
Zixiao Dong, Wei Yang, Zihao Liu +4
Most methods that optimize LLM prompts and agent workflows assume that task-specific output schemas, extraction instructions, and evaluation criteria are predefined. For scientific…
Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection
Pu Li, Tao Tan, Hong Xie +2
This paper considers the overestimation bias problem of Q-learning in the setting of a large action space, for the purpose of relieving the bottleneck of existing methods. We find…
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
Minzheng Wang, Run Luo, Yanbo Wang +6
While Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for closed-ended tasks, extending it to open-ended social language games via self-play reveals a cr…
AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature
Wei Yang, Zihao Liu, Tao Tan +6
This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural l…
Model Specific Task Similarity for Vision Language Model Selection via Layer Conductance
Wei Yang, Hong Xie, Tao Tan +3
While open sourced Vision-Language Models (VLMs) have proliferated, selecting the optimal pretrained model for a specific downstream task remains challenging. Exhaustive evaluation…
Demystifying Design Choices of Reinforcement Fine-tuning: A Batched Contextual Bandit Learning Perspective
Hong Xie, Xiao Hu, Tao Tan +5
The reinforcement fine-tuning area is undergoing an explosion papers largely on optimizing design choices. Though performance gains are often claimed, inconsistent conclusions also…
Rethinking Reinforcement fine-tuning of LLMs: A Multi-armed Bandit Learning Perspective
Xiao Hu, Hong Xie, Tao Tan +2
A large number of heuristics have been proposed to optimize the reinforcement fine-tuning of LLMs. However, inconsistent claims are made from time to time, making this area elusive…
Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing
Hong Xie, Haoran Gu, Yanying Huang +2
This paper proposes a variant of multiple-play stochastic bandits tailored to resource allocation problems arising from LLM applications, edge intelligence, etc. The model is compo…