4 citations · 6 across the 16 of their papers we have counts for
7 papers · 1 filter
From Imitation to Reward Discovery: On-Policy Warmup for Agentic RL
Yitong Qiao, Tiantian He, Lei Liu +4
Reinforcement learning with a verifiable reward (RLVR) offers a scalable approach to training language-model agents, yet sparse outcome rewards can leave early training with little…
EHR-RobustGym: Benchmarking and Training Agents for Robust Clinical Reasoning
Yitong Qiao, Yancheng Jin, Lei Liu +4
In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements referenced in a clinical query. E…
LiveClin: A Live Clinical Benchmark without Leakage
Xidong Wang, Shuqi Guo, Yue Shen +6
The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address the…
ClinAlign: Scaling Healthcare Alignment from Clinician Preference
Shiwei Lyu, Xidong Wang, Lei Liu +6
Although large language models (LLMs) demonstrate expert-level medical knowledge, aligning their open-ended outputs with fine-grained clinician preferences remains challenging. Exi…
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning
Junjie Wang, Zequn Xie, Dan Yang +9
Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe th…
TL-GRPO: Turn-Level RL for Reasoning-Guided Iterative Optimization
Peiji Li, Linyang Li, Handa Sun +15
Large language models have demonstrated strong reasoning capabilities in complex tasks through tool integration, which is typically framed as a Markov Decision Process and optimize…
Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response
Yitong Qiao, Licheng Pan, Yu Mi +6
Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in high-stakes environments. Howeve…