1 citations · 1 across the 8 of their papers we have counts for
8 papers
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…
Towards Universal Neural Likelihood Inference
Shreyas Bhat Brahmavar, Yang Li, Qiyang Liu +2
We introduce universal neural likelihood inference (UNLI): enabling a single model to provide data-grounded, conditional likelihood predictions for arbitrary targets given any coll…
Distribution Guided Active Feature Acquisition
Yang Li, Junier Oliva
Human agents routinely reason on instances with incomplete and muddied data (and weigh the cost of obtaining further features). In contrast, much of ML is devoted to the unrealisti…
Towards Cost Sensitive Decision Making
Yang Li, Junier Oliva
Many real-world situations allow for the acquisition of additional relevant information when making decisions with limited or uncertain data. However, traditional RL approaches eit…