4 papers
Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach
Xin Guo, Zijiu Lyu
This paper studies policy transfer, one of the well-known transfer learning techniques adopted in large language models, for continuous-time reinforcement learning problems. In the…
MindSpeed RL: Distributed Dataflow for Scalable and Efficient RL Training on Ascend NPU Cluster
Laingjun Feng, Chenyi Pan, Xinjie Guo +11
Reinforcement learning (RL) is a paradigm increasingly used to align large language models. Popular RL algorithms utilize multiple workers and can be modeled as a graph, where each…
DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
Jinxin Liu, Xinghong Guo, Zifeng Zhuang +1
In this paper, we propose a novel approach called DIffusion-guided DIversity (DIDI) for offline behavioral generation. The goal of DIDI is to learn a diverse set of skills from a m…
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Henry Weld, Guanghao Huang, Jean Lee +6
Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context. We introduce CONDA, a new dataset for in-game toxic langua…