10 papers
GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning
Xiang Cheng, Yulan Hu, Lulu Zheng +4
Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single user, where the field is approachin…
Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
Lulu Zheng, Wenjin Yang, Xiangwen Zhang +4
Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility aggregation, yielding unstable…
STEPS: Selective On-Policy Self-Distillation for Reasoning
Jiaxuan Wang, Xuan Ouyang, Zhiyu Chen +4
On-policy self-distillation (OPSD) uses a model as its own teacher under privileged context, providing token-level supervision on the model's own reasoning trajectories. However, p…
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
Jiaxuan Wang, Yulan Hu, Wenjin Yang +3
In classical Reinforcement Learning from Human Feedback (RLHF), Reward Models (RMs) serve as the fundamental signal provider for model alignment. As Large Language Models evolve in…
Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization
Xin Li, Yan Ke, Longbing Cao
ESG-aware portfolio optimization is increasingly important for sustainable capital allocation, yet most learning-based methods still operationalize ESG by appending static scores t…
ErrOPD: When Does On-Policy Distillation Transfer Teacher Errors?
Xin Li, Hao Jiang, Annan Wang +2
A distilled student can copy its teacher's mistakes along with its skills. On-policy distillation (OPD), used in recent post-training recipes, has the teacher grade every token of…
Learn More with Less: Uncertainty Consistency Guided Query Selection for RLVR
Hao Yi, Yulan Hu, Xin Li +3
Large Language Models (LLMs) have recently improved mathematical reasoning through Reinforcement Learning with Verifiable Reward (RLVR). However, existing RLVR algorithms require l…
AMAP Agentic Planning Technical Report
AMAP AI Agent Team, Yulan Hu, Xiangwen Zhang +22
We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and…
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning
Zhicong Li, Lingjie Jiang, Yulan Hu +7
Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However,…
Beyond Itinerary Planning-A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks
Xiang Cheng, Yulan Hu, Xiangwen Zhang +5
Travel planning is a natural real-world task to test large language models' (LLMs) planning and tool-use abilities. Although prior work has studied LLM performance on travel planni…