3 accepted papers
Large language models (LLMs) have recently advanced in reasoning when optimized with reinforcement learning (RL) under verifiable rewards. Existing methods primarily rely on outcome-based supervision to strengthen internal LLM reasoning, often leading to inefficient exploration and sparse rewards. T…
Recently, various successful applications utilizing expert states in imitation learning (IL) have been witnessed. However, IL from visual inputs (ILfVI), which has a greater promise to be widely applied by using online visual resources, suffers from low data-efficiency and poor performance resulted…
Recently, to reap the quantum advantage, empowering reinforcement learning (RL) with quantum computing has attracted much attention, which is dubbed as quantum RL (QRL). However, current QRL algorithms employ an online learning scheme, i.e., the policy that is run on a quantum computer needs to inte…