From the 1 of 9 linked papers with an AI index.
9 papers
AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao +10
Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13
SkillRise introduces a reinforcement‑learning framework that lets large language model agents learn and reuse transferable skills across related tasks by curating a skill document…
CAST: Game Solvers as Turn-Level Teachers for LLM Agents
Yu Wang, Yi-Kai Zhang, Wentao Shi +8
Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR)…
Look Before You Leap: Autonomous Exploration for LLM Agents
Ziang Ye, Wentao Shi, Yuxin Liu +6
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-…
MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning
Yuxin Liu, Ziang Ye, Yueqing Sun +6
Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforeh…
When2Speak: A Dataset for Temporal Participation and Turn-Taking in Multi-Party Conversations for Large Language Models
Vihaan Nama, Shreya Mendi, Zian Ye +1
Large Language Models (LLMs) excel at generating contextually appropriate responses but remain poorly calibrated for multi-party conversations, where deciding when to speak is as c…