Hugging Face Trending Papers
Jul 29

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?

Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be pretrained on offline data too? Conventional wisdom suggests it should, but recent results show that online RL with a randomly-initialized Q-function can result in highly performant and reliable policies without needing to pretrain the Q-function.

arXiv Machine Learning
Jul 30

Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?

arXiv:2607. 27203v1 Announce Type: new Abstract: Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be pretrained on offline data too?

By Perry Dong, Ron Polonsky, Dorsa Sadigh, Chelsea Fin
arXiv Machine Learning
5d ago

Q-Learning with Scalar Adjoint Matching

The paper introduces Q‑Learning with Scalar Adjoint Matching (SQAM), a method that replaces expensive per‑step vector–Jacobian products in flow policy fine‑tuning with a closed‑form scalar adjoint that scales the value gradient by flow time. SQAM also incorporates a value penalty on policy‑generated actions, which is shown to be crucial for effective learning. Experiments demonstrate that SQAM outperforms strong baselines on the hardest OGBench domains and improves supervised fine‑tuning on a real bimanual robot with a vision‑language‑action policy.

By Yonghoon Dong, Minsung Yoon, Jaehyuk Kim, Jungwoo Park, Changyeon Kim, Jinwoo Shin