7 papers · 1 filter
Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory
Zihao Cheng, Yingyu Shan, Hongru Wang +6
Travel planning agents assist users in generating personalized travel plans by modeling their individual preferences. Existing agents either rely on explicit user instructions or e…
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes
Yingyu Shan, Zeming Liu, Silin Li +4
Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progress…
Terminal-World: Scaling Terminal-Agent Environments via Agent Skills
Zihao Cheng, Hongru Wang, Zeming Liu +6
Terminal agents extend Large Language Models with the ability to execute tasks directly in command-line environments, but their progress is bottlenecked by the scarcity of high-qua…
Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
Jiashu Yao, Heyan Huang, Zeming Liu +1
To overcome the sparse reward challenge in reinforcement learning (RL) for agents based on large language models (LLMs), we propose Mutual Information Self-Evaluation (MISE), an RL…
MemEvolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation
Zihao Cheng, Zeming Liu, Yingyu Shan +7
While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typi…
Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
Jiashu Yao, Heyan Huang, Daiqing Wu +2
To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifur…
ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks
Jiashu Yao, Heyan Huang, Zeming Liu +4
Following formatting instructions to generate well-structured content is a fundamental yet often unmet capability for large language models (LLMs). To study this capability, which…