arXiv Machine Learning By Maxime Heuillet, Ola Ahmad, Audrey Durand

Neural Active Learning Meets the Partial Monitoring Framework

Read the original on arXiv Machine Learning →

arXiv:2405. 08921v2 Announce Type: replace Abstract: We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 21

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

arXiv:2509. 02522v3 Announce Type: replace-cross Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches.

By Jiaming Li, Longze Chen, Ze Gong, Yukun Chen, Lu Wang, Wanwei He, Run Luo, Min Yang