6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.AI2026
ClawTrace: Cost-Aware Tracing for LLM Agent Skill Distillation
Boqin Yuan, Yue Su, Renchu Song +2
Skill-distillation pipelines learn reusable rules from LLM agent trajectories, but they lack a key signal: how much each step costs. Without per-step cost, a pipeline cannot distin…
cs.AI2026
LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning
Chang Yao, Jinghui Qin, Kebing Jin +1
Despite achieving remarkable success in complex tasks, Deep Reinforcement Learning (DRL) is still suffering from critical issues in practical applications, such as low data efficie…
cs.LG2024★ 6 cited
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Daniel Bogdoll, Jing Qin, Moritz Nekolla +3
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. C…