3 papers
cs.LG2026
Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy
Jiashun Liu, Runze Liu, Xu Wan +3
Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses. Recent…
cs.LG2025
Efficient Morphology-Aware Policy Transfer to New Embodiments
Michael Przystupa, Hongyao Tang, Martin Jagersand +4
Morphology-aware policy learning is a means of enhancing policy sample efficiency by aggregating data from multiple agents. These types of policies have previously been shown to he…
cs.LG2025
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?
Hongyao Tang
Learning the optimal policy from a random network initialization is the theme of deep Reinforcement Learning (RL). As the scale of DRL training increases, treating DRL policy netwo…