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PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
Yipeng Shi, Zhipeng Ma, Yue Wang +4
In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimizati…
ATOD: Annealed Turn-Aware On-Policy Distillation for Multi-Turn Agentic Tasks
Qitai Tan, Zefang Zong, Mo Li +3
Training small language-model agents for long-horizon interactive tasks requires both fast imitation and reward-driven improvement. On-policy distillation (OPD) provides dense teac…
ATGPO: Agentic Turn-Group Policy Optimization with Adaptive Turn-level Clipping
Dingwei Chen, Zefang Zong, Zhipeng Ma +5
Reinforcement learning for agentic large language models (LLMs) typically relies on a sparse, trajectory-level outcome reward, making it difficult to evaluate the contribution of i…
Task Expansion and Cross Refinement for Open-World Conditional Modeling
Shreyas Bhat Brahmavar, Qiyang Liu, Yang Li +1
Open-world conditional modeling (OCM), requires a single model to answer arbitrary conditional queries across heterogeneous datasets, where observed variables and targets vary and…
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
Yuanjie Lu, Beichen Wang, Zhengqi Wu +4
Autonomous navigation in highly constrained environments remains challenging for mobile robots. Classical navigation approaches offer safety assurances but require environment-spec…