The paper presents an unsupervised multimodal reward model built on a frozen Qwen3‑VL backbone. By adding a linear head to an intermediate layer and training it to rank a web caption higher than a perturbed version of itself, the authors avoid human preference labels and achieve competitive performance—outperforming GPT‑4o on several benchmarks with far fewer parameters and compute. The approach demonstrates that a carefully chosen intermediate layer, simple perturbation-based training, and verifier‑driven fine‑tuning can yield effective vision‑language rewards without costly annotation.
By Yueying Li, Jingxuan Fan, Kiant\'e Brantley, Sham M. Kakade, Hanlin Zhang
Poolwise is a protocol that evaluates reward models for selecting training data across different prompts by choosing a fixed fraction of responses based on score and measuring the quality of the selected set with external labels. It reports within‑prompt ranking separately and shows that training each prompt’s mean score to match its average response rating improves selection over permuted or omitted mean targets in all tested seeds at 30% retention. In grouped HelpSteer experiments, the model with the highest within‑prompt accuracy selects a lower‑rated set than the correct‑mean model, and subtracting prompt means from reference responses preserves local rankings but reduces selected helpfulness for several reward models and judges.
whyItMatters":"Poolwise offers a direct method to compare reward models and score transformations for data selection, addressing the limitation of existing benchmarks that only compare responses within the same prompt."
By Mateusz Modrzejewski, Tomasz Radzikowski
arXiv:2607. 16195v1 Announce Type: new Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF).
By Elena Kopteva, Vitaliy Hlynianyi-Zhuk
arXiv:2606. 18963v1 Announce Type: new Abstract: We study online reward-punishment learning when the environment provides no scalar reward or evaluative label.
By Zirong Li
arXiv:2608.30005v1 Announce Type: new
Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific c...
By Fengyu Xie, Yilun Zhao, Bingsen Chen, Arman Cohan, Chen Zhao
arXiv:2606. 03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned and scalable proxies.
By Zelalem Abahana