<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mickel Liu | PAIR Lab: PKU Alignment and Interaction Research Lab</title><link>https://pair-lab.ai/author/mickel-liu/</link><atom:link href="https://pair-lab.ai/author/mickel-liu/index.xml" rel="self" type="application/rss+xml"/><description>Mickel Liu</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><image><url>https://pair-lab.ai/author/mickel-liu/avatar_hu478ac1947dd9452a2c71291f167cdad9_62517_270x270_fill_q75_lanczos_center.jpg</url><title>Mickel Liu</title><link>https://pair-lab.ai/author/mickel-liu/</link></image></channel></rss>hor/mickel-liu/</link></image><item><title>Omnisafe: An infrastructure for accelerating safe reinforcement learning research</title><link>https://pair-lab.ai/publication/jmlr_2024_2/</link><pubDate>Tue, 03 Sep 2024 00:00:00 +0000</pubDate><guid>https://pair-lab.ai/publication/jmlr_2024_2/</guid><description/></item><item><title>BeaverTails: A Human-Preference Dataset for LLM Harmlessness Alignment</title><link>https://pair-lab.ai/publication/neurips23db_2/</link><pubDate>Tue, 30 May 2023 00:00:00 +0000</pubDate><guid>https://pair-lab.ai/publication/neurips23db_2/</guid><description/></item><item><title>MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control</title><link>https://pair-lab.ai/publication/neurips_2022_7/</link><pubDate>Wed, 28 Sep 2022 00:00:00 +0000</pubDate><guid>https://pair-lab.ai/publication/neurips_2022_7/</guid><description/></item></channel></rss>