5 papers
The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
Haoyuan Xu, Chang Li, Xinyan Ma +12
Tool use enables large language models (LLMs) to access external information, invoke software systems, and act in digital environments beyond what can be solved from model paramete…
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models
Runxuan Liu, Xianhao Ou, Xinyan Ma +13
Long Chain-of-Thought (LCoT), achieved by Reinforcement Learning with Verifiable Rewards (RLVR), has proven effective in enhancing the reasoning capabilities of Large Language Mode…
Good Learners Think Their Thinking: Generative PRM Makes Large Reasoning Model More Efficient Math Learner
Tao He, Rongchuan Mu, Lizi Liao +3
Large reasoning models (LRMs) have recently shown promise in solving complex math problems when optimized with Reinforcement Learning (RL). But conventional approaches rely on outc…
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning
Tao He, Lizi Liao, Ming Liu +1
Recent advancements in dialogue policy planning have emphasized optimizing system agent policies to achieve predefined goals, focusing on strategy design, trajectory acquisition, a…
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues
Tao He, Lizi Liao, Yixin Cao +5
Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional t…