12 citations · 22 across the 3 of their papers we have counts for
3 papers
Improving Large Language Model Fine-tuning for Solving Math Problems
Yixin Liu, Avi Singh, C. Daniel Freeman +2
Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-…
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Homer Walke, Jonathan Yang, Albert Yu +4
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires…
i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops
Saminda Abeyruwan, Laura Graesser, David B. D'Ambrosio +6
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to train policies in simulation enables safe exploration and large-scale data collection…