8 citations · 9 across the 9 of their papers we have counts for
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cs.AI2024
ICPL: Few-shot In-context Preference Learning via LLMs
Chao Yu, Qixin Tan, Hong Lu +5
Preference-based reinforcement learning is an effective way to handle tasks where rewards are hard to specify but can be exceedingly inefficient as preference learning is often tab…
cs.CL2024★ 8 cited
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
Shusheng Xu, Wei Fu, Jiaxuan Gao +6
Reinforcement Learning from Human Feedback (RLHF) is currently the most widely used method to align large language models (LLMs) with human preferences. Existing RLHF methods can b…
cs.RO2024★ 1 cited
Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models
Yutao Ouyang, Jinhan Li, Yunfei Li +4
We present a large language model (LLM) based system to empower quadrupedal robots with problem-solving abilities for long-horizon tasks beyond short-term motions. Long-horizon tas…